Method, system and program product for defining and recording minimum and maximum event counts of a simulation utilizing a high level language

ABSTRACT

According to one method of simulation processing, instrumentation code, such as an runtime executive (rtx), receives one or more statements describing an count event and identifying the count event as an outlying count event. While simulating a design utilizing the HDL simulation model, occurrences of the outlying count event are counted to obtain a count event value. Simulation result data obtained from simulating the design is then received and processed. In the processing, the count event value is recorded within a data storage subsystem responsive to a determination of whether or not the count event value of the outlying count event exceeds a previously recorded count event value.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is also related to the following co-pending U.S.Patent applications:

-   -   (1) U.S. patent application Ser. No. 10/116,524, filed Apr. 4,        2002; and    -   (2) U.S. patent application Ser. No. 10/970,468, filed Oct. 21,        2004.

The above-mentioned patent applications are assigned to the assignee ofthe present invention and incorporated herein by reference in theirentireties.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates in general to designing and simulatingdigital devices, modules and systems, and in particular, to a method andsystem for computer simulation of digital devices, modules and systemsutilizing a hardware description language (HDL) model. Moreparticularly, the present invention relates to methods, systems, andprogram products for recording minimum and maximum events counts of asimulation.

2. Description of the Related Art

Verifying the logical correctness of a digital design and debugging thedesign, if necessary, are very important steps in most digital designprocesses. Logic networks are tested either by actually buildingnetworks or by simulating networks on a computer. As logic networksbecome highly complex, it becomes necessary to simulate a design beforethe design is actually built. This is especially true when the design isimplemented as an integrated circuit, since the fabrication ofintegrated circuits requires considerable time and correction ofmistakes is quite costly. The goal of digital design simulation is theverification of the logical correctness of the design.

In a typical automated design process that is supported by aconventional electronic computer-aided design (ECAD) system, a designerenters a high-level description utilizing a hardware descriptionlanguage (HDL), such as VHDL, producing a representation of the variouscircuit blocks and their interconnections. The ECAD system compiles thedesign description into a format that is best suited for simulation. Asimulator is then utilized to verify the logical correctness of thedesign prior to developing a circuit layout.

A simulator is typically a software tool that operates on a digitalrepresentation, or simulation model of a circuit, and a list of inputstimuli representing inputs of the digital system. A simulator generatesa numerical representation of the response of the circuit, which maythen either be viewed on the display screen as a list of values orfurther interpreted, often by a separate software program, and presentedon the display screen in graphical form. The simulator may be run eitheron a general-purpose computer or on another piece of electronicapparatus, typically attached to a general purpose computer, speciallydesigned for simulation. Simulators that run entirely in software on ageneral-purpose computer will hereinafter be referred to as “softwaresimulators”. Simulators that are run with the assistance of speciallydesigned electronic apparatus will hereinafter be referred to as“hardware simulators”.

Usually, software simulators perform a very large number of calculationsand operate slowly from the user's point of view. In order to optimizeperformance, the format of the simulation model is designed for veryefficient use by the simulator. Hardware simulators, by nature, requirethat the simulation model comprising the circuit description becommunicated in a specially designed format. In either case, atranslation from an HDL description to a simulation format, hereinafterreferred to as a simulation executable model, is required.

The results obtained from running a simulation testcase against asimulation model may vary depending upon not only factors within thesimulation model, but also factors outside of the simulation model.These external factors may include, for example, those controlling theset up of the simulation model or characteristics of the testcase.

A particular example of an external factor that influences simulationresults is testcase strategy. In order to exercise complex simulationmodels, such as those simulating the logical behavior ofmicroprocessors, testcases are commonly generated by testcase generationsoftware having hundreds or thousands of different input parameters.Each set of testcases created from a menu of common input parametervalues is called a testcase strategy. In order to achieve sufficienttest coverage, the testcase generation software may generate testcasesrepresenting tens of thousands of different testcase strategies.

Understandably, simulation engineers are very interested in determiningwhich testcase strategies provide the most useful simulation results sothat the less helpful strategies can be eliminated and the bulk ofsimulation jobs can be performed utilizing testcases generated utilizingthe better testcase strategies. In order to determine which testcasestrategies are preferred, it is helpful if simulation engineers can viewsimulation results, such as event counts, by testcase strategy.

The present invention also recognizes that it would be useful anddesirable for a simulation engineer or designer to know not only theaggregate number of occurrences of events in a simulation model, butalso the maximum and minimum number of occurrences of selected eventsduring a simulation run.

SUMMARY OF THE INVENTION

According to one embodiment of the present invention, a file describinga hardware description language (HDL) simulation model of a designincludes one or more statements describing a count event and identifyingthe count event as an outlying count event. While simulating the designutilizing the HDL simulation model, occurrences of the outlying countevent are counted to obtain a count event value. Simulation result dataobtained from simulating the design is then received and processed. Inthe processing, the count event value is recorded within a data storagesubsystem responsive to a determination of whether or not the countevent value of the outlying count event exceeds a previously recordedcount event value.

According to a second embodiment, instrumentation code, such as anruntime executive (rtx), receives one or more statements describing ancount event and identifying the count event as an outlying count event.While simulating a design utilizing the HDL simulation model,occurrences of the outlying count event are counted to obtain a countevent value. Simulation result data obtained from simulating the designis then received and processed. In the processing, the count event valueis recorded within a data storage subsystem responsive to adetermination of whether or not the count event value of the outlyingcount event exceeds a previously recorded count event value.

All objects, features, and advantages of the present invention willbecome apparent in the following detailed written description.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself however, as well as apreferred mode of use, further objects and advantages thereof, will bestbe understood by reference to the following detailed description of anillustrative embodiment when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is a pictorial representation of a data processing system;

FIG. 2 depicts a representative hardware environment of the dataprocessing system illustrated in FIG. 1;

FIG. 3A is a simplified block diagram illustrating a digital designentity that may be instrumented in accordance with the teachings of thepresent invention;

FIG. 3B is a diagrammatic representation depicting a simulation modelthat may be instrumented in accordance with the teachings of the presentinvention;

FIG. 3C is a flow diagram illustrating of a model build process that maybe implemented in accordance with the teachings of the presentinvention;

FIG. 3D is a block diagram depicting data structures that may beinstrumented in accordance with the teachings of the present invention;

FIG. 4A is a simplified block diagram representative of aninstrumentation entity;

FIG. 4B is a simplified block diagram of a simulation model instrumentedin accordance with the teachings of the present invention;

FIG. 4C illustrates exemplary sections of HDL syntax that maybe utilizedin accordance with the teachings of the present invention;

FIG. 4D is a flow diagram depicting a model build process in accordancewith the teachings of the present invention;

FIG. 4E is a block diagram representation of memory data structuresconstructed in accordance with the teachings of the present invention;

FIG. 5A is a logic diagram representation of a runtime disable mechanismin accordance with the teachings of the present invention;

FIG. 5B is a block diagram representation of functional units utilizedto execute the method and system of the present invention on a hardwaresimulator in accordance with the teachings of the present invention;

FIG. 6A is a simplified gate level representation of an exemplarycounting instrument with a runtime disable feature and automaticclocking adjustment in accordance with the teachings of the presentinvention;

FIG. 6B is a simplified timing diagram illustrating automatic clockingadjustment of counting instrumentation;

FIG. 7 depicts an alternative counting means that may be employed forcounting events detected by instrumentation entities in accordance withthe teachings of the present invention;

FIG. 8A illustrates a conventional finite state machine that may beinstrumented with an embedded checker in accordance with the teachingsof the present invention;

FIG. 8B depicts a conventional finite state machine design entity;

FIG. 8C illustrates a hardware description language file includingembedded instrumentation in accordance with the teachings of the presentinvention;

FIG. 9 depicts a hardware description language design entity includedembedded instrumentation in accordance with the teachings of the presentinvention;

FIG. 10A is a block diagram illustrating a simulation model containing anumber of design and instrumentation entities;

FIG. 10B depicts a data structure for declaring an event within asimulation model in accordance with one embodiment of the presentinvention;

FIG. 10C illustrates a list of extended event data structures for thesimulation model in FIG. 10A;

FIG. 10D depicts a data structure for declaring an event within asimulation model in accordance with an alternate embodiment of thepresent invention;

FIG. 11A is a block diagram illustrating a simulation model in which thehierarchical event processing of the present invention is applicable;

FIG. 11B depicts a set of input port mapping comments for performinghierarchical processing of simulation model events in accordance with afirst embodiment of the present invention;

FIG. 11C illustrates a set of input port mapping comments for performinghierarchical processing of simulation model events in accordance with asecond embodiment of the present invention;

FIG. 12A depicts a representative target design entity with aninstrumentation entity containing random instrumentation logicimplemented in accordance with the teachings of the present invention;

FIG. 12B illustrates an exemplary HDL file for implementinginstrumentation logic within an HDL design entity in accordance with theteachings of the present invention;

FIG. 13A depicts an exemplary design entity containing a multi-bitsimulation signal;

FIG. 13B illustrates a design entity wherein signal injection isimplemented in accordance with the teachings of the present invention;

FIG. 13C depicts an exemplary HDL source file that describesinstrumentation entity in accordance with the teachings of the presentinvention;

FIG. 13D illustrates an HDL design entity source code file wherein a setof random instrumentation comments implement the logic necessary forselectively overriding a simulation signal in accordance with theteachings of the present invention;

FIG. 14A is a block diagram depicting data content within a main memoryduring a simulation run of a simulation model;

FIG. 14B is a block diagram illustrating data contents of a main memoryduring a simulation run in accordance with the teachings of the presentinvention;

FIG. 14C depicts an exemplary HDL source code file that describes aninstrumentation entity in accordance with the teachings of the presentinvention;

FIG. 15 illustrates an eventlist file 1660 for the count events ofsimulation model 1000 shown in FIG. 10A;

FIG. 16A depicts a batch simulation farm in which a preferred embodimentof the present invention may be implemented;

FIG. 16B is a flow diagram illustrating a progression of events from thecreation of a specific simulation model to the removal of that modelfrom batch simulation farm and instrumentation server in accordance witha preferred embodiment of the present invention;

FIG. 16C is a flow diagram depicting steps performed during execution ofa simulation job within a batch simulation farm in accordance with apreferred embodiment of the present invention;

FIG. 17A is a block diagram illustrating the active data content withina main memory during a simulation run of a simulation model within abatch simulation farm environment in accordance with a preferredembodiment of the present invention;

FIG. 17B depicts an aggregate data packet delivered by an API entrypoint routine to an instrumentation server in accordance with apreferred embodiment of the present invention;

FIG. 17C is a flow diagram illustrating a process by which thecorrectness of aggregate data packets received by an batch simulationfarm instrumentation server is validated in accordance with a preferredembodiment of the present invention;

FIG. 18A illustrates memory contents of a simulation client duringexecution of a simulation job in accordance with a preferred embodimentof the present invention;

FIG. 18B is a flow diagram depicting steps performed by an API entrypoint in accessing a batch simulation farm instrumentation server toobtain a disable failure list in accordance with a preferred embodimentof the present invention;

FIG. 19A is a block diagram illustrating memory contents of a simulationclient at the conclusion of a simulation job in accordance with apreferred embodiment of the present invention;

FIG. 19B is a flow diagram depicted a process by which a batchsimulation farm instrumentation server processes fail event aggregatedata packets in accordance with a preferred embodiment of the presentinvention;

FIG. 20A is a block diagram illustrating the active memory content of asimulation client during simulation model testing in which count eventdata delivered to an instrumentation server within a batch simulationfarm environment in accordance with a preferred embodiment of thepresent invention;

FIG. 20B depicts an aggregate count event packet delivered by an APIentry point routine to an instrumentation server in accordance with apreferred embodiment of the present invention;

FIG. 20C illustrates a count storage file maintained within a batchsimulation farm instrumentation server in accordance with a preferredembodiment of the present invention;

FIG. 20D depicts a counter directory/subdirectory structure maintainedwithin a batch simulation farm instrumentation server in accordance witha preferred embodiment of the present invention;

FIG. 20E illustrates a count event entity translation table derived frommultiple entity list files in accordance with a preferred embodiment ofthe present invention;

FIG. 20F depicts a system applicable within a batch simulation farm forstoring and accessing count event data in accordance with a preferredembodiment of the present invention;

FIG. 20G illustrates a hierarchical and a non-hierarchical basic counteroutput report in accordance with the teachings of the present invention;

FIG. 20H depicts a user-modifiable counter query data structureapplicable to the counter storage and access system shown in FIG. 20F;

FIG. 20I is a flow diagram illustrating steps performed by a counterquery engine program to produce a basic counter output report from auser query in accordance with a preferred embodiment of the presentinvention;

FIG. 21A depicts a system applicable within a batch simulation farm forstoring and accessing trends in count event data in accordance with apreferred embodiment of the present invention;

FIG. 21B illustrates file and instruction means required for countdifference analysis in accordance with a preferred embodiment of thepresent invention;

FIG. 21C is a high-level flow diagram depicting steps performed within abatch simulation farm instrumentation server during count differenceanalysis processing in accordance with a preferred embodiment of thepresent invention;

FIG. 21D is a flow diagram illustrating steps performed within a batchsimulation farm instrumentation server during counter output reportcomparison processing in accordance with a preferred embodiment of thepresent invention;

FIG. 22A illustrates elements within a batch simulation farm utilized incollecting harvest event testcases in accordance with a preferredembodiment of the present invention;

FIG. 22B is a flow diagram depicting steps performed in collectingharvest event testcases in accordance with a preferred embodiment of thepresent invention;

FIG. 22C is a flow diagram illustrating the operation of a harvestmanager program during harvest event testcase collection in accordancewith a preferred embodiment of the present invention;

FIG. 23A depicts additional elements within an instrumentation serverand a harvest testcase server that are utilized in resolvinginconsistencies between a master harvest hit table and a harvesttestcase bucket in accordance with a preferred embodiment of the presentinvention;

FIG. 23B illustrates the data structure and content of a harvesttestcase list and a master harvest hit table as they exist prior to theharvest annealing process of the present invention;

FIG. 23C is a flow diagram illustrating in further detail the stepsperformed by a harvest manager program to process annealing requestsfrom a harvest testcase server in accordance with a preferred embodimentof the present invention;

FIG. 24A depicts data structure representations of API entry pointsdesigned to support the generation and operation of RTX instrumentationevents in accordance with a preferred embodiment of the presentinvention;

FIG. 24B is a block diagram illustrating memory contents utilized duringprocessing of a simulation job that employs RTX instrumentation eventsperformed with respect to an HDL simulation model;

FIG. 24C is a flow diagram depicting steps performed by an RTX duringthe generation and processing of RTX instrumentation events inaccordance with a preferred embodiment of the present invention;

FIG. 24D is a flow diagram illustrating in greater detail the operationof a trigger event API entry point in accordance with a preferredembodiment of the present invention;

FIG. 25A depicts a high-level block diagram representation of an HDLsimulation model augmented with a design entitylist data structure inaccordance with a preferred embodiment of the present invention;

FIG. 25B illustrates an instrumentation code module having designatedprogram entrypoints;

FIG. 25C depicts data structure representations of API entry pointsdesigned to manage dynamically loading and calling instrumentation codemodules in accordance with a preferred embodiment of the presentinvention;

FIG. 25D is a block diagram illustrating memory contents utilized duringprocessing of a simulation job that employs instrumentation code modulesprocessed with respect to an HDL simulation model in accordance with apreferred embodiment of the present invention;

FIG. 25E is a flow diagram depicting steps performed by an API entrypoint during loading and initialization of instrumentation code modules;

FIG. 25F is a flow diagram illustrating steps performed by an API entrypoint to execute instrumentation code modules during each sequence of amain simulation processing loop;

FIG. 26A depicts an exemplary section of HDL syntax that maybe utilizedto enumerate maximum and minimum counters in accordance with the presentinvention;

FIG. 26B illustrates an exemplary HDL file for implementinginstrumentation logic including a minimum counter within an HDL designentity in accordance with the present invention;

FIG. 26C depicts an exemplary eventlist file for minimum and maximumcount events of a simulation model in accordance with the presentinvention;

FIG. 26D illustrates a count event packet including minimum and maximumcount event values delivered by an API entry point routine to aninstrumentation server in accordance with the present invention; and

FIG. 26E depicts a high level logical flowchart of an exemplary processby which a count event packet containing maximum and minimum count eventvalues is processed by an instrumentation server in accordance with thepresent invention.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

The present invention provides for accurate and comprehensive monitoringof a digital circuit design in which a designer creates instrumentationmodules utilizing the same hardware description language (HDL) asutilized for the design itself. HDLs, while suited to the needs ofdigital designers can also be effectively utilized for a number ofchecking functions. In accordance with the Method and System of thepresent invention, instrumentation modules are utilized to monitorspecified design parameters while not becoming compiled as an integralpart of the design itself. Furthermore, since the instrumentationmodules are written in the same HDL as utilized in the actual design,such modules are platform and simulator independent. Unlike checkingdone with C or C++ programs, HDL instrumentation can be compiled and rundirectly without loss of performance on hardware simulators.

With reference now to the figures, and in particular with reference toFIG. 1, there is depicted a pictorial representation of a dataprocessing system 10 with which the present invention may beadvantageously utilized. As illustrated, data processing system 10comprises a workstation 12 to which one or more nodes 13 are connected.Workstation 12 preferably comprises a high performance multiprocessorcomputer, such as the RISC System/6000 or AS/400 computer systemsavailable from International Business Machines Corporation (IBM).Workstation 12 preferably includes nonvolatile and volatile internalstorage for storing software applications comprising an ECAD system,which can be utilized to develop and verify a digital circuit design inaccordance with the method and system of the present invention. Asdepicted, nodes 13 are comprised of a display device 14, a keyboard 16,and a mouse 20. The ECAD software applications executed withinworkstation 12 preferably display a graphic user interface (GUI) withindisplay screen 22 of display device 14 with which a digital circuitdesigner can interact using a keyboard 16 and mouse 20. Thus, byentering appropriate inputs utilizing keyboard 16 and mouse 20, thedigital circuit designer is able to develop and verify a digital circuitdesign according to the method described further hereinbelow.

FIG. 2 depicts a representative hardware environment of data processingsystem 10. Data processing system 10 is configured to include allfunctional components of a computer and its associated hardware. Dataprocessing system 10 includes a Central Processing Unit (“CPU”) 24, suchas a conventional microprocessor, and a number of other unitsinterconnected via system bus 26. CPU 24 includes a portion of dataprocessing system 10 that controls the operation of the entire computersystem, including executing the arithmetical and logical functionscontained in a particular computer program. Although not depicted inFIG. 2, CPUs such as CPU 24 typically include a control unit thatorganizes data and program storage in a computer memory and transfersthe data and other information between the various parts of the computersystem. Such CPUs also generally include an arithmetic unit thatexecutes the arithmetical and logical operations, such as addition,comparison, multiplications and so forth. Such components and units ofdata processing system 10 can be implemented in a system unit such asworkstation 12 of FIG. 1.

Data processing system 10 further includes random-access memory (RAM)28, read-only memory (ROM) 30, display adapter 32 for connecting systembus 26 to display device 14, and I/O adapter 34 for connectingperipheral devices (e.g., disk and tape drives 33) to system bus 26. RAM28 is a type of memory designed such that the location of data stored init is independent of the content. Also, any location in RAM 28 can beaccessed directly without having to work through from the beginning. ROM30 is a type of memory that retains information permanently and in whichthe stored information cannot be altered by a program or normaloperation of a computer.

Display device 14 provides the visual output of data processing system10. Display device 14 can be a cathode-ray tube (CRT) based videodisplay well known in the art of computer hardware. However, with aportable or notebook-based computer, display device 14 can be replacedwith a liquid crystal display (LCD) based or gas plasma-based flat-paneldisplay. Data processing system 10 further includes user interfaceadapter 36 for connecting keyboard 16, mouse 20, speaker 38, microphone40, and/or other user interface devices, such as a touch-screen device(not shown), to system bus 26. Speaker 38 is one type of audio devicethat may be utilized in association with the method and system providedherein to assist diagnosticians or computer users in analyzing dataprocessing system 10 for system failures, errors, and discrepancies.Communications adapter 42 connects data processing system 10 to acomputer network. Although data processing system 10 is shown to containonly a single CPU and a single system bus, it should be understood thatthe present invention applies equally to computer systems that havemultiple CPUs and to computer systems that have multiple buses that eachperform different functions in different ways.

Data processing system 10 also includes an interface that resides withina machine-readable media to direct the operation of data processingsystem 10. Any suitable machine-readable media may retain the interface,such as RAM 28, ROM 30, a magnetic disk, magnetic tape, or optical disk(the last three being located in disk and tape drives 33). Any suitableoperating system and associated interface (e.g., Microsoft Windows) maydirect CPU 24. For example, the AIX operating system and AIX Windowswindowing system can direct CPU 24. The AIX operating system is IBM'simplementation of the UNIX™ operating system. Other technologies alsocan be utilized in conjunction with CPU 24, such as touch-screentechnology or human voice control.

Those skilled in the art will appreciate that the hardware depicted inFIG. 2 may vary for specific design and simulation applications. Forexample, other peripheral devices such as optical disk media, audioadapters, or chip programming devices, such as PAL or EPROM programmingdevices well-known in the art of computer hardware and the like, may beutilized in addition to or in place of the hardware already depicted. Inaddition, main memory 44 is connected to system bus 26, and includes acontrol program 46. Control program 46 resides within main memory 44,and contains instructions that, when executed on CPU 24, carries out theoperations depicted in FIG. 4D and FIG. 4E described herein.

Simulated digital circuit design models are comprised of at least oneand usually many sub-units referred to hereinafter as design entities.FIG. 3A is a block diagram representation of an exemplary design entity300 in which the method and system of the present invention may beimplemented. Design entity 300 is defined by a number of components: anentity name, entity ports, and a representation of the functionperformed by design entity 300. Each entity within a given model has aunique name (not explicitly shown in FIG. 3A) that is declared in theHDL description of each entity. Furthermore, each entity typicallycontains a number of signal interconnections, known as ports, to signalsoutside the entity. These outside signals may be primary input/outputs(I/Os) of an overall design or signals connecting to other entitieswithin an overall design.

Typically, ports are categorized as belonging to one of three distincttypes: input ports, output ports, and bi-directional ports. Designentity 300 is depicted in as having a number of input ports 303 thatconvey signals into design entity 300. Input ports 303 are connected toinput signals 301. In addition, design entity 300 includes a number ofoutput ports 306 that convey signals out of design entity 300. Outputports 306 are connected to a set of output signals 304. Bi-directionalports 305 are utilized to convey signals into and out of design entity300. Bi-directional ports 305 are in turn connected to a set ofbi-directional signals 309. An entity, such as design entity 300, neednot contain ports of all three types, and in the degenerate case,contains no ports at all. To accomplish the connection of entity portsto external signals, a mapping technique, known as a “port map”, isutilized. A port map (not explicitly depicted in FIG. 3A) consists of aspecified correspondence between entity port names and external signalsto which the entity is connected. When building a simulation model, ECADsoftware is utilized to connect external signals to appropriate ports ofthe entity according to a port map specification.

Finally, design entity 300 contains a body section 308 that describesone or more functions performed by design entity 300. In the case of adigital design, body section 308 contains an interconnection of logicgates, storage elements, etc., in addition to instantiations of otherentities. By instantiating an entity within another entity, ahierarchical description of an overall design is achieved. For example,a microprocessor may contain multiple instances of an identicalfunctional unit. As such, the microprocessor itself will often bemodeled as a single entity. Within the microprocessor entity, multipleinstantiations of any duplicated functional entities will be present.

Referring now to FIG. 3B, there is illustrated a diagrammaticrepresentation of an exemplary simulation model 329 that may be utilizedin a preferred embodiment of the present invention. Simulation model 329consists of multiple hierarchical entities. For visual simplicity andclarity, the ports and signals interconnecting the entities withinsimulation model 329 have not been explicitly shown. In any model, oneand only one entity is the so-called “top-level entity”. A top-levelentity 320 is that entity which encompasses all other entities withinsimulation model 329. That is to say, top-level entity 320 instantiates,either directly or indirectly, all descendant entities within a design.Simulation model 329 consists of top-level entity 320 which directlyinstantiates two instances, 321 a and 321 b, of an FXU entity 321 and asingle instance of an FPU entity 322. Each instantiation has anassociated description, which contains an entity name and a uniqueinstantiation name. For top-level entity 320, description 310 is labeled“TOP:TOP”. Description 310 includes an entity name 312, labeled as the“TOP” preceding the colon, and also includes an instantiation name 314,labeled as the “TOP” following the colon.

It is possible for a particular entity to be instantiated multiple timesas is depicted with instantiations 321 a and 321 b of FXU entity 321.Instantiations 321 a and 321 b are distinct instantiations of FXU entity321 with instantiation names FXU0 and FXU1 respectively. Top-levelentity 320 is at the highest level within the hierarchy of simulationmodel 329. An entity that instantiates a descendant entity will bereferred to hereinafter as an “ancestor” of the descendant entity.Top-level entity 320 is therefore the ancestor that directlyinstantiates FXU entity instantiations 321 a and 321 b. At any givenlevel of a simulation model hierarchy, the instantiation names of allinstantiations must be unique.

In addition to FXU entity instantiations 321 a and 321 b, top-levelentity 320 directly instantiates a single instance of a FPU entity 322having an entity name FPU and instantiation name FPU0. Within an entitydescription, it is common for the entity name to match the instantiationname when only one instance of that particular entity is placed at agiven level of a simulation model hierarchy. However, this is notrequired as shown by entity 322 (instantiation name FPU0, entity nameFPU).

Within instantiation 321 a of FXU entity 321, single instance entities325 a and 326 a of entity A 325 and entity B 326 respectively, aredirectly instantiated. Similarly, instantiation 321 b of the same FXUentity contains instantiations 325 b and 326 b of entity A 325 andentity B 326 respectively. In a similar manner, instantiation 326 a andinstantiation 326 b each directly instantiate a single instance ofentity C 327 as entities 327 a and 327 b respectively. The nesting ofentities within other entities can continue to an arbitrary level ofcomplexity provided that all entities instantiated, whether singly ormultiply, have unique entity names and the instantiation names at anygiven level of the hierarchy are unique with respect to one another.Each entity is constructed from one or more HDL files that contain theinformation necessary to describe the entity.

Associated with each entity instantiation is a so called “instantiationidentifier”. The instantiation identifier for a given instantiation is astring consisting of the enclosing entity instantiation names proceedingfrom the top-level entity instantiation name. For example, theinstantiation identifier of instantiation 327 a of entity C 327 withininstantiation 321 a of FXU entity 321 is “TOP.FXU0.B.C”. This identifierserves to uniquely identify each instantiation within a simulationmodel.

Referring now to FIG. 3C, there is depicted a flow diagram of a modelbuild process which may be implemented in a preferred embodiment of thepresent invention. The process begins with one or more design entity HDLsource code files 340 and, potentially, one or more design entityintermediate format files 345, hereinafter referred to as “proto files”345, available from a previous run of an HDL compiler 342. HDL compiler342 processes HDL file(s) 340 beginning with the top level entity of asimulation model and proceeding in a recursive fashion through all HDLor proto file(s) describing a complete simulation model. For each of HDLfiles 340 during the compilation process, HDL compiler 342, examinesproto files 345 to determine if a previously compiled proto file isavailable and consistent. If such a file is available and consistent,HDL compiler 342 will not recompile that particular file, but willrather refer to an extant proto file. If no such proto file is availableor the proto file is not consistent, HDL compiler 342 explicitlyrecompiles the HDL file 340 in question and creates a proto file 344,for use in subsequent compilations. Such a process will be referred tohereinafter as “incremental compilation” and can greatly speed theprocess of creating a simulation executable model 348. Incrementalcompilation is described in further detail hereinbelow. Once created byHDL compiler 342, Proto files 344 are available to serve as proto files345 in subsequent compilations.

In addition to proto files 344, HDL compiler 342 also creates two setsof data structures, design entity proto data structures 341 and designentity instance data structures 343, in memory 44 of computer system 10.Design entity proto data structures 341 and design entity instance datastructures 343, serve as a memory image of the contents of a simulationexecutable model 348. Data structures 341 and 343 are passed, via memory44, to a model build tool 346 that processes data structures 341 and 343into simulation executable model 348.

It will be assumed hereinafter that each entity is described by a singleHDL file. Depending on convention or the particular HDL in which thecurrent invention is practiced, this restriction may be required.However, in certain circumstances or for certain HDLs it is possible todescribe an entity by utilizing more than one HDL file. Those skilled inthe art will appreciate and understand the extensions necessary topractice the present invention if entities are permitted to be describedby multiple HDL files. Furthermore, it will be assumed that there is adirect correspondence, for each entity, between the entity name and bothof the following: the name of the HDL file representing the entity, andthe name of the proto file for the entity.

In the following description, an HDL source code file corresponding to agiven entity will be referred to by an entity name followed by “.vhdl”.For example, the HDL source code file that describes top-level entity320 will be referred to as TOP.vhdl. This labeling convention serves asa notational convenience only and should not be construed as limitingthe applicability of the present invention to HDLs other than VHDL.

Returning to FIG. 3B, it can be seen that each entity may instantiate,either directly or indirectly, one or more other entities. For example,the FXU entity directly instantiates A entity 325 and B entity 326.Furthermore, B entity 326 directly instantiates C entity 327. Therefore,FXU entity 321 instantiates, directly or indirectly, A entity 325, Bentity 326 and C entity 327. Those entities, that are directly orindirectly instantiated by another entity, will be referred tohereinafter as “descendants”. The descendants of top level entity 320are FXU entity 321, FPU entity 322, A entity 325, B entity 326, and Centity 327. It can be seen that each entity has a unique set ofdescendants and that each time an entity is instantiated, a uniqueinstance of the entity and its descendants is created. Within simulationmodel 329, FXU entity 321 is instantiated twice, FXU:FXU0 321 a andFXU:FXU1 321 b, by top-level entity 320. Each instantiation of FXUentity 321 creates a unique set of instances of the FXU, A, B, and Centities.

For each entity, it is possible to define what is referred to as a“bill-of-materials” or BOM. A BOM is a list of HDL files having date andtime stamps of the entity itself and the entity's descendants. Referringagain to FIG. 3C, the BOM for an entity is stored in proto file 344after compilation of the entity. Therefore, when HDL compiler 342compiles a particular HDL source code file among HDL files 340, a protofile 344 is generated that includes a BOM listing the HDL files 340 thatconstitute the entity and the entity's descendants, if any. The BOM alsocontains the date and time stamp for each of the HDL files referenced aseach appeared on disk/tape 33 of computer system 10 when the HDL filewas being compiled.

If any of the HDL files constituting an entity or the entity'sdescendants is subsequently changed, proto file 344 will be flagged asinconsistent and HDL compiler 342 will recompile HDL file 340 on asubsequent re-compilation as will be described in further detail below.For example, going back to FIG. 3B, the HDL files referenced by the BOMof FXU entity 321 are FXU.vhdl, A.vhdl, B.vhdl and C.vhdl, each withappropriate date and time stamps. The files referenced by the BOM oftop-level entity 320 are TOP.vhdl, FXU.vhdl, A.vhdl, B.vhdl, C.vhdl, andFPU.vhdl with appropriate date and time stamps.

Returning to FIG. 3C, HDL compiler 342 creates an image of the structureof a simulation model in main memory 44 of computer system 10. Thismemory image is comprised of the following components: “proto” datastructures 341 and “instance” data structures 343. A proto is a datastructure that, for each entity in the model, contains information aboutthe ports of the entity, the body contents of the entity, and a list ofreferences to other entities directly instantiated by the entity (inwhat follows, the term “proto” will be utilized to refer to thein-memory data structure described above and the term “proto file” willbe utilized to describe intermediate format file(s) 344). Proto files344 are therefore on-disk representations of the in-memory proto datastructure produced by HDL compiler 342.

An instance data structure is a data structure that, for each instanceof an entity within a model, contains the instance name for theinstance, the name of the entity the instance refers to, and the portmap information necessary to interconnect the entity with externalsignals. During compilation, each entity will have only one proto datastructure, while, in the case of multiple instantiations of an entity,each entity may have one or more instance data structures.

In order to incrementally compile a model efficiently, HDL compiler 342follows a recursive method of compilation in which successive entitiesof the model are considered and loaded from proto files 345 if suchfiles are available and are consistent with the HDL source filesconstituting those entities and their descendants. For each entity thatcannot be loaded from existing proto files 345, HDL compiler 342recursively examines the descendants of the entity, loads thosedescendant entities available from proto file(s) 345 and creates, asneeded, proto files 344 for those descendants that are inconsistent withproto files 345. Pseudocode for the main control loop of HDL compiler342 is shown below (the line numbers to the right of the pseudocode arenot a part of the pseudocode, but merely serve as a notationalconvenience).

process_HDL_file(file) 5 { 10   if (NOT proto_loaded(file)) { 15     if(exists_proto_file(file) AND check_bom(file)) { 20      load_proto(file); 25     } else { 30       parse_HDL_file(file) 35      for (all instances in file) { 40        process_HDL_file(instance); 45       } 50      create_proto(file); 55       write_proto_file(file); 60     } 65  } 70   create_instance(file): 75 } 80

When compiler 342 is initially invoked, no proto data structures 341 orinstance data structures 343 are present in memory 44 of computer system10. The main control loop, routine process_HDL_file( ) (line 5), isinvoked and passed the name of the top level entity by means ofparameter “file”. The algorithm first determines if a proto datastructure for the current entity is present in memory 44 by means ofroutine proto_loaded( ) (line 15). Note that the proto data structurefor the top level entity will never be present in memory because theprocess starts without any proto data structures loaded into memory 44.If a matching proto data structure is present in memory 44, instancedata structures for the current entity and the current entity'sdescendants, if any, are created as necessary in memory 44 by routinecreate_instance( ) (line 75).

However, if a matching proto data structure is not present in memory 44,control passes to line 20 where routine exists_proto_file( ) examinesproto files 345 to determine if a proto file exists for the entity. Ifand only if a matching proto file exists, routine check_bom( ) is calledto determine whether proto file 345 is consistent. In order to determinewhether the proto file is consistent, the BOM for the proto file isexamined. Routine check_bom( ) examines each HDL source code file listedin the BOM to determine if the date or time stamps for the HDL sourcecode file have changed or if the HDL source code file has been deleted.If either condition occurs for any file in the BOM, the proto file isinconsistent and routine check_bom( ) fails. However, if check_bom( ) issuccessful, control is passed to line 25 where routine load_proto( )loads the proto file and any descendant proto files into memory 44, thuscreating proto data structures 341 for the current entity and thecurrent entity's descendants, if any. The construction ofprocess_HDL_file( ) ensures that once a proto file has been verified asconsistent, all of its descendant proto files, if any, are alsoconsistent.

If the proto file is either non-existent or is not consistent, controlpasses to line 35 where routine parse_HDL_file( ) loads the HDL sourcecode file for the current entity. Routine parse_HDL_file( ) (line 35)examines the HDL source code file for syntactic correctness anddetermines which descendant entities, if any, are instantiated by thecurrent entity. Lines 40, 45, and 50 constitute a loop in which theroutine process_HDL_file( ) is recursively called to process thedescendent entities that are called by the current entity. This processrepeats recursively traversing all the descendants of the current entityin a depth-first fashion creating proto data structures 341 and protodata files 344 of all descendants of the current entity. Once thedescendant entities are processed, control passes to line 55 where a newproto data structure is created for the current entity in memory 44 byroutine create_proto( ). Control then passes to line 60 where a newproto file 344, including an associated BOM, is written to disk 33 byroutine write_proto_file( ). Finally, control passes to line 75 whereroutine create_instance( ) creates instance data structures 343 for thecurrent entity and any descendant entities as necessary. In this manner,process_HDL_file( ) (line 5) recursively processes the entire simulationmodel creating an in-memory image of the model consisting of proto datastructures 341 and instance data structures 343.

With reference now to FIG. 3D there is depicted a block diagramrepresenting compiled data structures, which may be implemented in apreferred embodiment of the present invention. Memory 44 contains protodata structures 361, one for each of the entities referred to insimulation model 329. In addition, instantiations in simulation model329 are represented by instance data structures 362. Instance datastructures 362 are connected by means of pointers indicating thehierarchical nature of the instantiations of the entities withinsimulation model 329. Model build tool 346 in FIG. 3C processes thecontents of memory 44 into memory data structures in order to producesimulation executable model 348.

In order to instrument simulation models, the present invention makesuse of entities known as “instrumentation entities,” which are incontrast to the entities constituting a design which are referred toherein as “design entities”. As with design entities, instrumentationentities are described by one or more HDL source code files and consistof a number of signal ports, a body section, and an entity name. In whatfollows, it will be assumed that an instrumentation entity is describedby a single HDL file. Those skilled in the art will appreciate andunderstand extensions necessary to practice the current invention for aninstrumentation entity that is described by multiple HDL files. Eachinstrumentation entity is associated with a specific design entityreferred to hereinafter as the “target entity”.

With reference now to FIG. 4A, there is illustrated a block diagramrepresentation of an instrumentation entity 409. Instrumentation entity409 includes a number of input ports 400 that are connected to signals401 within a target entity (not depicted in FIG. 4A). A body section 402contains logic necessary to detect occurrences of specified conditionswithin the target entity and generate simulation model “events” withrespect to signals 401. Three distinct types of events may be generated:“count” events, “fail” events, and “harvest” events, each describedbelow in turn. Body section 402 contains internal logic for detectingoccurrences of conditions precipitating generation of these events. Aset of multi-bit output ports 403, 404, and 405 are connected toexternal instrumentation logic (depicted in FIG. 4B) by means ofexternal signals 406, 407, and 408. Output ports 403, 404, and 405 thusprovide the connection from the internal logic in body section 402 tothe external instrumentation logic which is utilized to indicate theoccurrence of count, failure and harvest events.

A failure event is a sequence of signal values that indicate a failurein the correct operation of the simulation model. Each instrumentationentity monitors the target entity for any desired number of failureevents. Each occurrence of a failure event is assigned to a particularsignal bit on output port 403. Logic within body section 402 produces anactive high pulse on a specified bit of signal 403 when a failurecondition is detected. Such activation of signal 403 is defined as afailure event. This error indication is conveyed by means of externalsignal 406 to external instrumentation logic (depicted in FIG. 4B asexternal instrumentation logic block 420), which flags the occurrence ofthe failure event.

A count event is a sequence of signal values that indicate theoccurrence of an event within a simulation model for which it would beadvantageous to maintain a count. Count events are utilized to monitorthe frequency of occurrence of specific sequences within a simulationmodel. Each instrumentation entity can monitor the target entity for anydesired number of count events. Each count event is assigned to aparticular signal bit on output port 405. Logic block 402 contains thelogic necessary to detect the occurrence of the desired count events andproduces an active high pulse on the specified bit of signal 405 when acount event is detected. This count indication is conveyed by means ofexternal signal 408 to instrumentation logic, which contains countersutilized to record the number of occurrences of each count event.

The third event type, a harvest event, is a sequence of signal valuesthat indicate the occurrence of a specific operative circumstance, whichwould be advantageous to be able to reproduce. When a harvest eventoccurs, a register within an external instrumentation logic block isloaded to indicate at what point within a simulation run the eventoccurred, and a flag is set to indicate the occurrence of the specificcircumstance. The details of the simulation run can thus be saved inorder to recreate the specific circumstance monitored by the harvestevent. Logic block 402 contains the logic necessary to detect theharvest events.

Each instrumentation entity can detect any desired number of harvestevents that are each assigned to a particular signal bit on output port404. Logic within block 402 produces an active high pulse on thespecified bit of signal 404 when a harvest event is detected. Thisharvest event detection is conveyed by means of external signal 407 toexternal instrumentation logic that contains a register and flag foreach harvest event. The register is utilized to record at which point inthe simulation run the harvest event occurred, and the flag is utilizedto indicate the occurrence.

With reference now to FIG. 4B, there is depicted a block diagramrepresentation of simulation model 329 instrumented in accordance withthe teachings of the present invention. As can be seen in FIG. 4B, aninstance 410 and an instance 411 of an instrumentation entity FXUCHK areutilized to monitor instances 321 a and 321 b of an FXU entity. For eachFXU instantiations of 321 a and 321 b, an FXUCHK instantiation, 410 and411 respectively, is automatically generated by the mechanism of thepresent invention. In a similar fashion, instrumentation entity FPUCHK412 is instantiated to monitor FPU entity 322.

As depicted in FIG. 4B, entity FXUCHK monitors a signals Q 372, a signalR 376, and a signal S 374 within each of instances 321 a and 321 b ofthe FXU entity. Signal Q 372, is a signal within the instances 325 a and325 b of descendant entity A. Likewise, signal S 374 is a signal withindescendant entity C that resides within descendant entity B. Finally,signal R 376 occurs directly within FXU entity 321. Although aninstrumentation entity may monitor any signal within a target entity orthe target entity's descendent entities, signals outside the targetentity cannot be monitored.

Each instrumentation entity is connected by means of fail, count, andharvest signals to instrumentation logic block 420 containing logic forrecording occurrences of each of the three event types. For the countevents monitored in simulation model 329, a set of counters 421 isutilized to count the number of occurrences of each count event. In asimilar manner, a set of flags 424 is utilized to record the occurrenceof failure events. Finally, a set of counters 422 and flags 423 arecombined and utilized to record the point at which a harvest eventoccurs and its occurrence, respectively. In one embodiment of thepresent invention, a cycle number is captured and stored utilizingcounters 422 and flags 423 to record a harvest event.

To facilitate instantiation and connection of instrumentation entities,instrumentation entity HDL source code files include a specializedcomment section, hereinafter referred to as “instrumentation entitydescription”, that indicates the target entity, the signals within thetarget entity to be monitored, and information specifying types ofevents to be monitored.

With reference now to FIG. 4C, there is illustrated an exemplary HDLfile 440 that describes instrumentation entity FXUCHK depicted in FIG.4B. HDL file 440 utilizes the syntax of the VHDL hardware descriptionlanguage. In the VHDL language, lines beginning with two dashes (“--”)are recognized by a compiler as being comments. The method and system ofthe present invention utilize comments of a non-conventional form toindicate information about an instrumentation entity. FIG. 4C depictsone embodiment of the present invention in which comments begin with twoexclamation points in order to distinguish these comments fromconventional comments in instrumentation HDL file 440. It will beappreciated by those skilled in the art that the exemplary syntaxutilized in FIG. 4C for the provision of unconventional comments is butone of many possible formats.

Within HDL file 440, the I/O ports of a FXUCHK entity are declared inentity declaration 450. Within entity declaration 450, three inputports, S_IN, Q_IN, and R_IN, respectively, are declared. Input ports,S_IN, Q_IN, and R_IN, will be attached to signal S, 374, signal Q, 372,and signal R, 376 respectively as described below. Input port, CLOCK, isalso declared and will be connected to a signal, CLOCK, within the FXUentity. In addition, three output ports: fails (0 to 1), counts(0 to 2),and harvests(0 to 1), are declared. These output ports provide failure,count, and harvest signals for two failure events, three count events,and two harvest events. The names of the output ports are fixed byconvention in order to provide an efficient means for automaticallyconnecting these signals to instrumentation logic block 420.

A set of instrumentation entity descriptors 451 is utilized to provideinformation about the instrumentation entity. As illustrated in FIG. 4C,descriptor comments 451 may be categorized in a number of distinctsections: prologue and entity name declaration 452, an input port map453, a set of failure message declarations 454, a set of counterdeclarations 455, a set of harvest declarations 456, and an epilogue457.

The prologue and entity name 452 serve to indicate the name of theparticular target entity that the instrumentation entity will monitor.Prologue and entity name declaration 452 also serves as an indicationthat the instrumentation entity description has begun. Specifically, thecomment “--!! Begin” within prologue and entity name 452, indicates thatthe description of an instrumentation entity has begun. The comment“--!! Design Entity: FXU” identifies the target entity which, in HDLfile 440, is design entity FXU. This declaration serves to bind theinstrumentation entity to the target entity.

Input port map 453 serves as a connection between the input ports of aninstrumentation entity and the signals to be monitored within the targetentity. The comments begin with comment “--!! Inputs” and end withcomment “--!! End Inputs”. Between these comments, comments of the form“--!! inst_ent_port_name => trgt_ent_signal_name” are utilized, one foreach input port of the instrumentation entity, to indicate connectionsbetween the instrumentation entity ports and the target entity signals.The inst_ent_port_name is the name of the instrumentation entity port tobe connected to the target entity signal. The trgt_ent_signal_name isthe name of the signal within the target entity that will be connectedto the instrumentation entity port.

In some cases a signal to be monitored lies within a descendant of atarget entity. This is the case for signal S 374, which is embeddedwithin entity C which is a descendant of entity B 326 and target FXUentity 321. Input port map 453 includes an identification string forsignal S 374, which consists of the instance names of the entitieswithin the target entity each separated by periods (“.”). Thisidentification string is pre-pended to the signal name. The signalmapping comment within input port map 453 for signal S 374 is thereforeas follows:

-   -   --!! S_IN => B.C.S;

This syntax allows an instrumentation entity to connect to any signalwithin the target entity or the target entity's descendant entities. Asignal appearing on the top level of the target design entity, has nopre-pended entity names; and therefore, has the following signal mappingcomment:

-   -   --!! R_IN => R;

For signals on the top level of the target entity, a special connectionmethod is provided. If the signal to be connected to has the same nameas its corresponding signal in the port map of the instrumentationentity, no input port mapping comment is required and the signal will beautomatically connected if no such comment is present. In other words,if the input port mapping comment is of the form:

-   -   --!! signal => signal;        where signal is a legal signal name without periods (“.”), then        the input port mapping comment is not required and the system of        the present invention will automatically make the connect. It is        also possible to provide comments of the form given above to        explicitly denote the signal connection. This mechanism is only        operative for signals on the top level of the target entity.

Failure message declarations 454 begin with a comment of the form “--!!Fail Outputs;”, and end with a comment of the form “--!! End FailOutputs;”. Each failure event output is associated with a unique eventname and a failure message. This message may be output by the simulationrun-time environment upon detecting a failure event. The unique failureevent name is utilized to identify the specific failure event within themodel. Each failure event signal may be declared by a comment of theform “--!! n: <eventname> “failure message”;” where n is an integerdenoting the failure event to which the message is associated,<eventname> is the unique failure event name, and “failure message” isthe message associated with the particular failure event. One and onlyone failure message declaration comment must be provided for eachfailure event monitored by the instrumentation entity.

Counter declaration comments 455 begin with a comment of the form “--!!Count Outputs;” and end with a comment of the form “--!! End CountOutputs;”. Each count event output is associated with a unique variablename. This name is associated with a counter in counter logic 421 FIG.4B. The variable name provides a means to identify and reference theparticular counter associated with a particular count event. Thus, acomment of the form “--!! n : <varname> qualifying_signal [+/−];” isassociated with each counter event output. Within this convention, n isan integer denoting which counter event in the instrumentation module isto be associated with a variable name “varname,” and qualifying_signalis the name of a signal within a target design entity utilized todetermine when to sample the count event pulse, as will be furtherdescribed hereinbelow. The parameter “qualifying_signal” is followed byA+/−A to specify whether the qualifying signal will be a high activequalifying signal or a low active qualifying signal.

Harvest declarations 456 begin with a prologue comment of the form “--!!Harvest Outputs;” and end with a comment of the form “--!! End HarvestOutputs;”. Each harvest event output is associated with a unique eventname and a message that may be output by the simulation runtimeenvironment when a harvest event has occurred during a simulation run.Each harvest event signal is declared in the form “--!! n: eventname>“harvest message”;” where n is an integer denoting which harvest eventthe message is to be associated with, <eventname> is the unique harvestevent name and “harvest message” is the message to be associated withthe particular harvest event. One, and only one, harvest messagedeclaration comment must be provided for each harvest event monitored bythe instrumentation entity.

Harvest messages and event names, fail messages and event names, andcounter variable names for a simulation model are included in asimulation executable model and lists of all the events within the modelare produced in separate files at model build time. In this manner, eachsimulation model includes the information for each event monitored and aseparate file containing this information for each event is available.Furthermore, as will be described below, the model build process nameseach event within the model (count, fail and harvest) model in such amanner as to insure that each event has a unique name with certainuseful properties.

Finally, epilogue comment 457 consists of a single comment of the form“--!! End;”, indicating the end of descriptor comments 451. Theremainder of instrumentation entity HDL file 440 that follows the I/Odeclarations described above is an entity body section 458. In entitybody section 458, conventional HDL syntax is utilized to define internalinstrumentation logic necessary to detect the various events on theinput port signals and convey these events to the output port signals.

In addition to descriptor comments 451, that are located in the HDLsource code file for an instrumentation entity, an additional commentline is required in the target entity HDL file. A comment of the form“--!! Instrumentation: name.vhdl”, where name.vhdl is the name of theinstrumentation entity HDL file, is added to the target entity HDLsource code file. This comment provides a linkage between theinstrumentation entity and its target entity. It is possible to havemore than one such comment in a target entity when more than oneinstrumentation entity is associated with the target entity. These HDLfile comments will hereinafter be referred to as “instrumentation entityinstantiations”.

With reference now to FIG. 4D, there is depicted a model build processin accordance with the teachings of the present invention. In this modelbuild process, instrumentation load tool 464 is utilized to alter thein-memory proto and instance data structures of a simulation modelthereby adding instrumentation entities to the simulation model.Instrumentation load tool 464 utilizes descriptor comments 451 withininstrumentation HDL files 461 to create instance data structures for theinstrumentation entities within a simulation model.

The model build process of FIG. 4D begins with design entity HDL files340 and, potentially, one or more design entity proto files 345(available from a previous run of HDL compiler 462), instrumentationentity HDL files 460, and potentially, one or more instrumentationentity proto files 461 (available from a previous run of HDL compiler462). HDL compiler 462, processes design entity HDL files 340, andinstrumentation entity HDL files 460 following an augmentation ofalgorithm process_HDL_file( ) that provides for efficient incrementalcompilation of the design and instrumentation entities comprising asimulation model. HDL compiler 462 loads proto data structures fromdesign entity proto files 345 and instrumentation entity protos files460, if such proto files are available and consistent. If such protofiles are not available or are not consistent, HDL compiler 462 compilesdesign entity HDL files 340 and instrumentation entity HDL files 460 inorder to produce design entity proto files 344 and instrumentationentity proto files 468. (Design entity proto files 344 andinstrumentation entity proto files 468 are available to serve as designentity proto files 345 and instrumentation entity proto files 460respectively for a subsequent run of HDL compiler 462.)

In addition, HDL compiler 462 creates in-memory design proto datastructures 463 and design instance data structures 465 for the designentities of a simulation model. HDL compiler 462 also creates in-memoryinstrumentation proto data structures 466 for the instrumentationentities of a simulation model.

In order to minimize processing overhead HDL compiler 462 neither readsnor processes descriptor comments 451. However, HDL compiler 462 doesrecognize instrumentation entity instantiation comments within targetentity HDL files. As such, HDL compiler 462 cannot create instance datastructures instrumentation entity data structures 467. The creation ofinstance data structures requires interconnection information containedwithin descriptor comments 451 not processed by HDL compiler 462. HDLcompiler 462 does, however, create instrumentation proto data structures466.

The in-memory design proto data structures 463, design instance datastructures 465, and instrumentation entity proto data structures 466,are processed by instrumentation load tool 464. Instrumentation loadtool 464 examines design entity proto data structures 463 and designentity instance data structures 465 to determine those design entitiesthat are target entities. This examination is accomplished by utilizinga particular comment format as previously described.

All target entities that are loaded from design entity proto files 345contain an instantiation for any associated instrumentation entity.Therefore, instrumentation load tool 464 merely creates an instance datastructure 467 for any such instrumentation entity and passes, theunaltered design proto data structure 463 to instrumented design protodata structure 463 a, and passes design instance data structure 465 toinstrumented design instance data structure 465 a.

If however, a target entity is loaded from design entity HDL files 340,rather than from design entity proto files 345, instrumentation loadtool 464 must alter its design proto data structure 463 and its designinstance data structure 465 to instantiate an associated instrumentationentity. An instrumented design proto data structure 463 a andinstrumented design instance data structure 465 a are thereby produced.In addition, instrumentation load tool 464 creates an instrumentationinstance data structure 467 for each instrumentation entity associatedwith the current design entity.

The design entity proto data structures 463 that are altered byinstrumentation load tool 464 are saved to disk 33 of computer system 10as design entity proto files 344. Design entity proto files 344, whichmay include references to instrumentation entities, are directly loadedby a subsequent compilation of a simulation model, thus savingprocessing by instrumentation load tool 464 on subsequent recompilationsunless an alteration is made to a design entity or an associatedinstrumentation entity.

In order for HDL compiler 462 to determine if alterations were made toeither a target design entity or the target design entity's associatedinstrumentation entities, the BOM of a target design entity is expandedto include the HDL files constituting the instrumentation entities. Inthis manner, HDL compiler 462 can determine, by inspection of the BOMfor a given design entity, whether to recompile the design entity andthe design entity's associated instrumentation entities or load thesestructures from proto files 345 and 461.

Finally, instrumentation load tool 464 creates a unique proto andinstance data structure for instrumentation logic block 420 and connectsthe fail, harvest, and count event signals from each instrumentationentity instantiation to instrumentation logic block 420. Model buildtool 446 processes in-memory proto and instance data structures 463 a,465 a, 467, 466 to produce instrumented simulation executable model 480.

In HDL compiler 462, algorithm process_HDL_file( ) is augmented to allowfor the incremental compilation of design and instrumentation entities.A pseudocode implementation of a main control loop of HDL compiler 462is shown below:

process_HDL_file2(file,design_flag) 5 { 10   if (NOT proto_loaded(file)){ 15     if (exists_proto_file(file) AND check_bom(file)) { 20    load_proto(file); 25   }else { 30     parse_HDL_file(file) 35    for (all instances in file) { 40       process_HDL_file2(instance,design_flag); 45     } 50     if (design_flag=TRUE) { 55         for(all instrumentation instances in file){ 60        process_HDL_file2(instance, FALSE); 65       } 70     } 75    create_proto(file); 80     write_proto_file(file); 90   } 95 } 100if (design_flag=TRUE) { 105 create_instance(file); 110   } 115 } 120

Algorithm process_HDL_file2( ) is an augmentation to process_HDL_file( )of HDL compiler 342 in order to support the creation of instrumentedsimulation models. The algorithm is invoked with the name of the toplevel design entity passed through parameter file and a flag indicatingwhether the entity being processed is a design entity or aninstrumentation entity passed through parameter design_flag(design_flag=TRUE for design entities and FALSE for instrumentationentities). Algorithm process_HDL_file2( ) (line 5) first checks, bymeans of routine proto_loaded( ) (line 15), if the proto for the currententity is already present in memory 44. If so, processing passes to line105. Otherwise, control is passed to line 20 and 25 where disk 33 ofcomputer system 10 is examined to determine if proto files for theentity and its descendants (including instrumentation entities, if any)exist and are consistent. If so, the appropriate proto files are loadedfrom disk 10 by routine load_proto( ) (line 25) creating proto datastructures, as necessary, in memory 44 for the current entity and thecurrent entity's descendants including instrumentation entities.

If the proto file is unavailable or inconsistent, control passes to line35 where the current entity HDL file is parsed. For any entitiesinstantiated within the current entity, lines 40 to 55 recursively callprocess_HDL_file2( ) (line 5) in order to process these descendants ofthe current entity. Control then passes to line 55 where the design_flagparameter is examined to determine if the current entity being processedis a design entity or an instrumentation entity. If the current entityis an instrumentation entity, control passes to line 80. Otherwise, thecurrent entity is a design entity and lines 60 to 70 recursively callprocess_HDL_file2( ) (line 5) to process any instrumentation entitiesinstantiated by means of instrumentation instantiation comments. Itshould be noted that algorithm process_HDL_file2( ) (line 5) does notallow for instrumentation entities to monitor instrumentation entities.Any instrumentation entity instantiation comments within aninstrumentation entity are ignored. Control then passes to line 80 whereproto data structures are created in memory 44 as needed for the currententity and any instrumentation entities. Control then passes to line 90where the newly created proto data structures are written, as needed todisk 33 of computer system 10.

Control finally passes to line 105 and 110 where, if the current entityis a design entity, instance data structures are created as needed forthe current entity and the current entity's descendants. If the currententity is an instrumentation entity, routine create_instance( ) (line110) is not called. Instrumentation load tool 464 is utilized to createthe in-memory instance data structures for instrumentation entities.

It will be apparent to those skilled in the art that HDL compiler 462provides for an efficient incremental compilation of design andinstrumentation entities. It should also be noted that the abovedescription is but one of many possible means for accomplishing anincremental compilation of instrumentation entities. In particular,although many other options also exist, much, if not all, of thefunctionality of instrumentation load tool 464 can be merged into HDLcompiler 462.

With reference now to FIG. 4E wherein is shown a depiction of memory 44at the completion of compilation of simulation model 329 withinstrumentation entities FXUCHK and FPUCHK. Memory 44 contains protodata structures 481, one for each of the design and instrumentationentities referred to in simulation model 329. In addition, design andinstrumentation instances in simulation model 329 are represented byinstance data structures 482. The instance data structures are connectedby means of pointers indicating the hierarchical nature of theinstantiations of the design and instrumentation entities withinsimulation model 329.

With reference now to FIG. 5A, failure flags 424 of instrumentationlogic block 420 are depicted in greater detail. Failure flags 424consist of registers 500 a-500 n utilized to accept and store anindication of the occurrence of a failure event. In what follows, theoperation of a single failure flag for a particular failure event 502will be discussed. The operation of all failure flags is similar.

Register 500 a holds a value that represents whether a failure event 502has occurred or not. Register 500 a is initially set to a value of ‘0’by the simulation run-time environment at the beginning of a simulationrun. When failure event 502, if enabled at register 507 a, occurs,register 500 a is set to a value of a logical>1′, thereby indicating theoccurrence of a failure event. Register 500 a is driven by logical ORgate 501. Logical OR gate 501 performs a logical OR of the output ofregister 500 a and a qualified failure signal 503 to create the nextcycle value for register 500 a. In this manner, once register 500 a isset to a logical>1′ by the occurrence of an enabled failure event,register 500 a maintains the value of a logical>1′ until reset by thesimulation runtime environment. Likewise, register 500 a maintains avalue of ‘0’ from the beginning of the simulation run until theoccurrence of the failure event, if enabled.

Qualified failure signal 503 is driven by logical AND gate 505. LogicalAND gate 505 produces, on qualified failure signal 503, the logical ANDof failure signal 506 and the logical NOT of register 507 a. Register507 a serves as an enabling control for qualified failure signal 503. Ifregister 507 a contains a value of ‘0’, logical AND gate 505 will passfailure event signal 506 unaltered to qualified failure signal 503. Inthis manner, the monitoring of the failure event is enabled. Registers507 a-507 n are set, by default, to a value of ‘0’. However, if register507 a contains a value of a logical>1′, qualified failure signal 503will remain at a value of ‘0’ irrespective of the value of failure eventsignal 506, thereby disabling the monitoring of failure event 502. Inthis manner, register 508, consisting of registers 507 a-507 n, can maskthe occurrence of any subset of failure events in the overall simulationmodel from registers 500 a-500 n.

To efficiently implement the ability to selectively disable themonitoring of failure events, the simulation run-time environmentincludes a function that allows a user to disable monitoring of aspecific failure event for a given instrumentation entity: This functionwill automatically set the appropriate registers among registers 507a-507 n within register 508 to disable the monitoring of a particularfailure event for every instance of the instrumentation entity withinthe overall simulation model. Instrumentation load tool 464 and modelbuild tool 446 encode sufficient information within instrumentedsimulation executable model 480 to determine which failure bits withinregister 508 correspond to which instrumentation entities.

The ability to selectively disable monitoring of failure events is ofparticular use in large batch-simulation environments. Typically, insuch an environment, a large number of general purpose computers,running software or hardware simulators, are dedicated to automaticallyrunning a large number of simulation runs. If a simulation model with afaulty instrumentation entity that incorrectly indicates failure eventsis run in such an environment, a large number of erroneous failures willbe generated causing lost time. By selectively disabling failure eventswithin instrumentation entities, the present invention allows simulationto continue while only disabling erroneous failure signals rather thanhaving to disable all failure monitoring. This option is particularlyuseful when the process of correcting a faulty instrumentation entityand creating a new simulation model is substantially time consuming. Thepresent invention also provides similar enabling and disablingstructures for the harvest and count events within a model.

Logical OR gate 512 is utilized to produce a signal 511, which indicateswhether any failure event within the model has occurred. This signal isutilized to allow hardware simulators to efficiently simulate simulationmodels that have been instrumented according to the teachings of thepresent invention.

With reference now to FIG. 5B there is illustrated in greater detail,features of the present invention utilized to support efficientexecution of an instrumented simulation model on a hardware simulator.It should be noted that for most hardware simulators, the operation ofpolling a facility within a simulation model during a simulation run isoften a time consuming operation. In fact, if facilities must be polledevery cycle, it is often the case that as much time, if not considerablymore, is spent polling a simulation model for results rather thanrunning the actual simulation. As such, it is advantageous when using ahardware simulator to avoid polling facilities within the model during asimulation run. In addition, many hardware simulators provide a facilitythat instructs the hardware simulator to run a simulation withoutinterruption until a specific signal within the simulation model attainsa specific value. This facility usually results in the highestperformance for a simulation run on a hardware simulator.

In order to execute simulation model 520 on a hardware simulator, atermination signal 513, is typically utilized as a means to avoid havingto poll the model after each cycle. Typically, a hardware simulator willcycle simulation model 520 until signal 513 is asserted to a logical>1′.The assertion of termination signal 513 to a logical>1′ indicates that asimulation run has finished. Without termination signal 513, it would benecessary to directly poll facilities within simulation model 520 todetermine when a simulation run is completed.

To efficiently locate and diagnose problems in simulation model 520, itis advantageous to allow a simulation run to be stopped immediatelywhenever a failure event occurs during simulation of simulation model520 (harvest events and count events are typically only polled at theend of a simulation run). This allows a user to easily locate thefailure event within the simulation run, thereby facilitating debuggingof the failure. In order to allow simulation models that have beeninstrumented according to the teachings of the present invention toefficiently execute on a hardware simulator, a comment of the form “--!!Model Done: signalname” is placed within the HDL source code file forthe top level entity of the simulation model where signalname is thename of termination signal 513 within the simulation model. This commentis only utilized if present in the HDL file for the top-level entity. Ifsuch a comment is present in the HDL source code file for the top levelentity, a logical OR gate 515 will automatically be included within thesimulation model. Logical OR gate 515 produces the logical OR of signals511 and 513 on signal 516. Signal 516 is therefore asserted to alogical>1′ whenever the simulation run has completed (signal 513 high)or a failure event has occurred (signal 511 high). Consequently, byexecuting simulation model 520 in a hardware simulator until signal 516is asserted to a value of a logical>1′, the instrumentation forsimulation model 520 can be combined and utilized along with existingsimulation termination techniques in a seamless manner. In thealternative, if the comment indicating the name of termination signal513 is not present, logical OR gate 515 is not included in the model andsignal 511 is directly connected to signal 516. The name of signal 516is fixed to a particular name by convention.

In many simulators, the passage of time within the simulated model ismodeled on a cycle-to-cycle basis. That is to say, time is considered topass in units known as cycles. A cycle is delineated by the occurrenceof a clock signal within a simulation model that regulates the updatingof storage elements within the design. These simulators are commonlyknown as “cycle simulators”. A cycle simulator models a digital designby repeatedly propagating the values contained within storage elementsthrough interconnecting logic that lies between storage elements withoutspecific regard for the physical timing of this propagation, to producenext cycle values within the storage elements. In such simulators, aprimitive storage element, hereinafter referred to as a “simulatorlatch”, is utilized to model the storage elements within a digitaldesign. One simulator cycle therefore consists of propagating thecurrent values of the simulator latches through the interconnectinglogic between storage elements and updating the simulator latches withthe next cycle value.

In many circumstances, however, it is not possible to utilize a singlesimulator latch to directly model the storage elements within a design.Many common storage elements utilized within digital designs oftenrequire more than one simulator latch. For example, so calledmaster-slave flip-flops are generally modeled utilizing two simulatorlatches to accurately simulate the behavior of such storage elements. Inorder to efficiently model storage elements, a designer will typicallyrefer to a library that contains storage element simulation models foruse in a design. These design storage elements are modeled by one ormore simulator latches. Storage elements comprised of one or moresimulator latches that are implemented within a design will be referredto hereinbelow as “design latches”.

As a consequence of utilizing multiple simulator latches to model adesign latch, the process of propagating the input of a design latch toits output, which constitutes a design cycle, often requires more thanone simulator cycle. A single design cycle is thus defined as comprisingthe number of simulator cycles required to propagate a set of valuesfrom one set of storage elements to the next.

In other circumstances, a simulation model may consist of distinctportions that are clocked at differing frequencies. For example, amicroprocessor core connected to a bus interface unit, may operate at ahigher frequency and than the bus interface unit. Under thesecircumstances, the higher frequency portion of the design will requireone or more simulator cycles, say N cycles, to simulate a single designcycle. The lower frequency portion of the design will require a multipleof N simulator cycles in order to simulate a design cycle for the lowerfrequency portion. This multiple is equal to the ratio of the frequencyof the higher speed design portion to the frequency of the lower speeddesign portion. It is often the case that certain portions of the logiccan be run at a number of differing frequencies that are selectable atthe beginning of a simulation run. Such logic, with a run-timeselectable frequency of operation, presents unique challenges formonitoring count events.

With reference now to FIG. 6A, there is depicted a gate levelrepresentation of exemplary logic for one counter of counters 421 withininstrumentation logic block 420 depicted in FIG. 4B. Each counter of 421is represented by a multi-bit simulator latch 600. Simulator latch 600is initialized by the simulation runtime environment to a value of zeroat the beginning of a simulation run. Simulator latch 600 is updatedevery simulator cycle and is driven by multiplexer 601. Multiplexer 601,controlled by selector signal 602, selects between signal 613, thecurrent value of simulator latch 600, and signal 605, the current valueof simulator latch 600 incremented by 1 by incrementer 604, to serve asthe next cycle value for simulator latch 600. By selecting signal 605,multiplexer 601 causes the counter value within simulator latch 600 tobe incremented when a count event occurs. It should be noted, however,that simulator latch 600 is updated every simulator cycle irrespectiveof the number of simulator cycles that correspond to a design cycle forthe logic being monitored by a counting instrument. Logical AND gate 606and simulator latch 607 serve to disable the monitoring of count eventsignal 609 in a manner similar to that described above for the disablingof failure events. Signal 608 is count event signal 609 furtherqualified by signal 610 by means of logical AND gate 611.

Signal 610 insures that simulator latch 600 will be incremented, ifcount event signal 609 is active, only once per design cycle for thelogic being monitored by a counting instrument irrespective of thenumber of simulation cycles utilized to model the design cycle. Thisclocking normalization is necessary to ensure that the event countsrecorded in counters 421 correspond directly to the number of designcycles the event occurred in and not the number of simulator cycles theevent occurred in. For example if an event occurs in two design cycleswhere design cycle require four simulators cycles, it is preferable tohave the event counter reflect a value of two rather than a value ofeight as would occur if the counter were allowed to update in everysimulator cycle.

Furthermore, if the count event being monitored is within a portion ofthe logic with a run-time selectable frequency of operation, it isuseful to have the count registers reflect the number of occurrences ofthe event in terms of design cycles. For example, consider acircumstance where a count event occurs twice during two differentsimulation runs. In the first run, assume that four simulator cycles areneeded to represent each design cycle. Further assume in the second runthat twelve simulator cycles are necessary to represent each designcycle. Without a clocking normalization mechanism, the first run wouldindicate that the event occurred eight times (two occurrences times foursimulator cycles per occurrence) and the second run would indicate thatthe event occurred twenty-four times (two occurrences times twelvesimulator cycles per occurrence) when in fact the event actually onlyoccurred twice in both simulation runs. Therefore, it would beadvantageous to limit the updating of counters 421 such that eachcounter is only updated once per design cycle irrespective of the numberof simulator cycles, possibly variable at run-time, needed to representa design cycle.

In simulation models in which multiple simulator cycles are utilized torepresent a single design cycle, explicit clocking signals are utilizedwithin the model to control the updating of the various design storageelements. These clocking signals specify in which simulator cycles thesimulator latches representing design storage elements are allowed toupdate. A clocking signal is asserted high for some contiguous number ofsimulator cycles at either the beginning or end of the design cycle andasserted low for the remaining simulator cycles within the design cycle.If the clocking signal is asserted high during the beginning of thedesign cycle, the clock is referred to as a “high-active” clock and,likewise, if the clocking signal is asserted low during the beginning ofthe design cycle, the clock is referred to as a “low-active” clock.

Each count event signal has an associated qualifying signal as specifiedby counter declaration comments 455 as described above. Typically, thesequalifying signals are connected to the clocking signals within thedesign responsible for updating the storage elements within the portionof logic monitored by the count event. The qualifying signal for thecount event for simulator latch 600, qualifying signal 612, is depictedas a high-active qualifier signal. Qualifying signal 612 is processed bysimulator latch 613 and logical AND gate 614, to produce signal 610which is active high for one and only one simulator cycle within thedesign cycle delineated by qualifying signal 612.

Turning now to FIG. 6B there is illustrated a simplified timing diagramthat demonstrates operation of simulator latch 613 and logical AND gate614 assuming clocking qualifying signal 612 is a high active clockingsignal of fifty percent duty cycle for a design cycle that occurs over a10-simulation cycle period. Signal 615, the output of simulator latch613, is qualifying signal 612 delayed by one simulator cycle. Signal 615is inverted and logically ANDed with qualifying signal 612 to producesignal 610, a high-active pulse that is asserted for the first simulatorcycle of the design cycle. In a similar fashion, if the qualifying clocksignal is low active, qualifying signal 612 would be inverted and signal615 would be uninverted by logical AND gate 614. This would produce asingle simulator cycle active high pulse during the first simulatorcycle of the design cycle. Qualifying signal 610, by qualifying countevent signal 609 by means of logical AND gate 611, insures that counter600 is incremented only once per design cycle irrespective of the numberof simulator cycles utilized to represent a design cycle.

In contrast to cycle simulators, another class of simulators know as“event-driven” simulators is commonly utilized. In an event drivensimulator, time is modeled in a more continuous manner. Each rising orfalling edge of a signal or storage element within a design is modeledwith specific regard to the physical time at which the signal transitionoccurred. In such simulators, the simulator latches operate in aslightly different manner than for a cycle based simulator. A simulatorlatch in an event driven simulator is controlled directly by a clockingsignal. A new value is loaded into the simulator latch on either therising or falling edge of the clocking signal (called a “positive-edgetriggered” latch and a “negative-edge triggered” latch respectively). Topractice the current invention within an event driven simulator, latch613 and logical gates 614 and 611 are unnecessary. Rather, counter latch600 is replaced by a positive or negative edge triggered simulator latchbased on the polarity of qualifying signal 612. Qualifying signal 612 isconnected directly to simulator latch 600 and directly controls theupdates of counter latch 600 insuring that the latch is updated onlyonce per design cycle.

Returning to FIG. 6A, incrementer 604 represents but one possiblemechanism that may be utilized to implement the next logic state for agiven counter within the present invention. As depicted in FIG. 6A,incrementer 604 ensures that counters 421 within a model are cycledthrough a series of values whose binary patterns correspond to thecustomary representation of non-negative integers. In one embodiment ofthe present invention, incrementer 604 is comprised of an adder thatincrements the current value of counter 600 by a unit value each timesignal 605 is selected by selector signal 602. This exemplaryimplementation provides for convenience of decoding the value of counter600 at the termination of a simulation run, but does so at a cost inoverhead that is not acceptable in many simulators.

For software simulators, one of two basic approaches may be utilized tomodel an incrementer, such as incrementer 604. In the first approach,the incrementer is modeled directly by an ADD or INCREMENT instructionin the simulation execution model. When incrementers are modeleddirectly as a single instruction within the simulation execution model,the use of incrementer 604 provides for efficient counters within asimulation execution model.

However, many software simulators and virtually all hardware simulatorsmodel incrementer functions as a set of gates that are replicatedessentially without change at each bit position of the counter. Within asoftware simulator, these gates must be translated into a sequence ofinstructions. In a hardware simulator, these gates are explicitlyreplicated for each counter as individual gates. Due to implementationor structural limitations, many software simulators are incapable ofmodeling an incrementer in any other manner than as a set of gates.Clearly, for these software simulators that must model incrementers as anumber of gates and therefore as a sequence of instructions, aperformance loss will result over those software simulators that modelincrementers as a single increment or add instruction. Likewise, forhardware simulators, the number of gates required for each adder, whichmust be modeled directly by gates within the hardware simulator, canprove to be a significant burden.

The method and system of the present invention alleviate thesedifficulties by implementing a linear feedback shift register as thecounting means within counting instrumentation. As explained below,appropriate configuration and utilization of such a liner feedback shiftregister results in an efficient method of incrementing a counter thatavoids the overhead associated with incrementer 604.

With reference now to FIG. 7, there is depicted a linear feedback shiftregister (LFSR) counter 700 consisting of a shift register 704 and“exclusive NOR” (XNOR) gate 706. Various methods of constructing LFSRsare known to those skilled in the art. As illustrated in FIG. 7, LFSRcounter 700 includes a modified shift register 704 that may replaceregister 600 and incrementer 604 of FIG. 6A. LFSR counter 700 alsoincludes multiplexer 601 (replicated bit-by-bit within LFSR 704), whichprovides feedback paths 616. Feedback path 616 provides a means forshift register 704 to maintain its current value during those simulatorcycles in which no count pulse trigger (signal 602) is received. Forhardware and software design simulators in which, for logistical orother reasons, incrementation of counters must be accomplished utilizinga set of gates for each counter, shift register 704 replaces register600 within the counter logic depicted in FIG. 6A. The need forincrementer 604 is thus eliminated and is replaced by XNOR gate 706. Inthis manner, register 600 and incrementer 604 are replaced utilizing amore efficient logic structure having substantially reduced overhead.Counters 421 of FIG. 4B will therefore consist of LFSR-basedconfigurations such as LFSR counter 700 whose values can be decoded atthe end of a simulation run to reveal their corresponding integralvalues.

Shift register 704 can be of any desired length. In a preferredembodiment, shift register 704 is a 22 bit register, although larger orsmaller registers may be employed. Shift register 704 consists oflatches 718 arranged in a serial fashion such that a given latch'soutput is utilized as input to the next latch 718 within shift register704. In addition, each of a select subset of latches 718 within shiftregister 704 has its output sourced to XNOR gate 706. XNOR gate 706 isutilized to provide an input for the first latch within shift register704.

The LFSR is a logic structure that, when properly configured, willsequence through all possible bit patterns with the exception of theall-ones pattern (it is possible to construct LFSRs which exclude theall-zeros pattern or LFSRs that cycle through all possible bitpatterns). For example, in a 22-bit LFSR, bits 1 and 22 may be selectedfor inputs to XNOR gate 706 to provide a sequence of bit patterns inshift register 704, which traverses every possible permutation with theexception of the all-ones pattern. Shift register 704 must be loadedwith an initial value that is not the all ones pattern. This may beaccomplished automatically by initializing all latches to a binary zerovalue within the simulator, or by utilizing the control program thatdrives the simulator to explicitly set these latches to binary zeros.

After initialization, the numeric pattern held by bit positions 718 ofshift register 704 will cycle through a specific and predictable patternin a repeating fashion. That is to say, for any given bit patternpresent in shift register 704, there is a specific, unique pattern theshift register will subsequently assume upon being shifted andtherefore, the sequence of patterns through which the shift registercycles is fixed and repeats in a predictable manner. Due to theseproperties, LFSR counter 700 can be utilized as a counting means withinfor the instrumentation detection means previously described. Byassigning the value of “zero” to a pre-selected starting value (say theall zeros pattern for shift register 704), the value of “one” to thenext bit pattern formed by shifting the LFSR, and so on, the LFSR canserve as a counter. To be useful as a counter, the bit patterns withinshift register 704 must be converted back to their corresponding integervalues. This is easily accomplished for LFSRs with a small number ofbits (less than 25 bits) by means of a lookup table consisting of anarray of values, where the index of the array corresponds to the LFSRbit pattern value and the entry in the array is the decoded integervalue for the LFSR. For LFSRs with a larger number of bits, softwaredecoding techniques can be utilized to decode the LFSR value bysimulating the operation of the LFSR.

As illustrated in FIG. 7, the logic necessary to implement LFSR counter700 consists of the single XNOR gate 706 with two feedback inputs. Whilethe number of required feedback gates and inputs thereto may vary inproportion to different possible lengths of an LFSR, in general, fortypical LFSRs (less than 200 bits), only one XNOR gate with a relativelysmall number of inputs (less than 5 bits) is required. This is in markedcontrast to the several logic gates per bit required for conventionalincrementers. Therefore, significant savings in counter overhead can beachieved by substituting LFSR-based counter 700 for the incrementerstructure depicted in FIG. 6A, especially for simulators that modelincrementers utilizing logic gate based representations.

While the above described system and method provides a practical meansof instrumenting simulation models, in certain circumstances additionaltechniques may be used in order to enhance the ease with which a usermay instrument a simulation model. In design, it often occurs that thereare common design or instrumentation logic constructs that are oftenrepeated and possess a regular structure.

By utilizing knowledge of the regular structure of these design andinstrumentation logic constructs, it is often possible to define asyntax that describes the instrumentation logic with considerablygreater efficiency than would be possible utilizing a conventional HDLconstruct. By utilizing this syntax as an unconventional HDL commentwithin a design VHDL file, it is possible to create instrumentationentities with considerably greater ease and efficiency.

Such comments within a design entity will be referred to hereinbelow asan embedded instrumentation entity comment while the instrumentationlogic created by such a comment will be referred to as an embeddedinstrumentation entity.

A common logic design construct is the so-called “finite state machine”.A finite state machine typically consists of a number of storageelements to maintain the “state” of the state machine and combinatoriallogic that produces the next state of the state machine and its outputs.These constructs occur with great frequency in typical logic designs andit is advantageous to be able to efficiently instrument theseconstructs.

A typical set of count and failure events for a finite state machineincludes counting the number of times a state machine cycles from agiven current state to some next state, counting the number offunctional cycles the state machine spends in each state, ensuring thatthe state machine does not enter an illegal state, and ensuring that thestate machine does not proceed from a current given state to an illegalnext state. This list of events is but one of many possible sets ofevents that can be used to characterize a finite state machine and isused in an illustrative manner only.

With reference now to FIG. 8A there is depicted a representation of anexemplary state machine 800. Exemplary state machine 800 consists offive states, labeled S0, S1, S2, S3, and S4 respectively, and nine legalstate transitions between these states. In what follows, it is assumedthat state machine 800 consists of three latches and a set ofcombinatorial logic to produce the next state function. It is furtherassumed that the states are encoded into the three latches following theusual and customary encoding for integers. That is to say, state S0 getsan encoding of 000_(bin), state S1 gets an encoding of 001_(bin), stateS2 gets and encoding of 010_(bin), and so on.

With reference now to FIG. 8B there is shown an exemplary design entity850 referred to as entity FSM with instance name FSM, which contains oneinstance of state machine 800. Furthermore, a signal output 801,“fsm_state(0 to 2)” contains a three bit signal directly connected tothe outputs of the three storage elements comprising the state elementsof state machine 800. A signal input 802, fsm_clock, applies a clockingsignal that controls the storage elements for state machine 800.

In order to instrument state machine 800, it would conventionally benecessary to create an instrumentation entity VHDL file containing thelogic necessary to detect the desired state machine events and pass themthrough to count and fail events. Such an instrumentation entity filewith appropriate instrumentation entity descriptor comments wouldtypically require substantially more lines of code than the HDLdescription of the state machine itself. Such a circumstance isundesirable. However, in the case of a regular logic structure such as afinite state machine, it is possible to define a brief syntax thatcharacterizes the finite state machine without resorting to a separateinstrumentation VHDL entity.

With reference now to FIG. 8C there is illustrated an exemplary HDL file860 for generating design entity 850 with an embedded instrumentationentity for monitoring the behavior of FSM 800. Specifically, an embeddedinstrumentation entity comment 852 is illustrated. As depicted in FIG.8C, embedded instrumentation entity comment 852 comprises a number ofdistinct sections including: a prologue and embedded instrumentationname declaration 853, a state machine clock declaration 859, a stateelement declaration 854, a state naming declaration 855, a state elementencoding declaration 856, a state machine arc declaration 857, and anepilogue 858.

Prologue and embedded instrumentation entity name declaration comment853 serves to declare a name that is associated with this embeddedinstrumentation entity. This comment line also serves to delineate thebeginning of an embedded instrumentation entity comment sequence.

As further depicted in FIG. 8C, declaration comment 853 assumes anon-conventional syntax of the form: “--!! Embedded TYPE: name”, wherein“--!! Embedded” serves to declare an embedded instrumentation entity,“TYPE” declares the type of the embedded instrumentation entity B FSM inthis case, and “name” is the name associated with this embeddedinstrumentation entity.

State machine clock declaration comment 859 is utilized to define asignal that is the clocking control for the finite state machine.

State element declaration comment 854 is utilized to specify thestate-machine state storage elements. This comment declares the storageelements or signal names that constitute the state-machine state. Instate machine 800, the signals fsm_state(0 to 2) constitute the statemachine state information.

State naming declaration comment 855 is utilized to declare labels toassociate with various states of the given state machine. These labelsare utilized in state machine arc declaration comment 857 when definingthe legal state transitions within the given state machine.

State element encoding declaration comment 856 is utilized to define acorrespondence between the state machine labels defined by state namingdeclaration comment 855 and the facilities declared by state elementdeclaration comment 854. In the example shown, the labels of comment 855are associated by position with the encodings given in comment 856(i.e., the state labeled “S0” has the encoding 000_(bin), the statelabeled “S1” has the encoding 001_(bin), etc.).

State-machine arc declaration comment 857 defines the legal statetransitions within the state machine. The various transitions of thestate machine are given by terms of the form “X => Y” where X and Y arestate machine state labels given by comment 855 and X represents aprevious state machine state and Y a subsequent state machine state.

Epilogue comment 858 serves to close the embedded instrumentation entitycomment. The specific syntax and nature of the comments between theprologue and embedded instrumentation name declaration and the epiloguewill vary with the specific needs of the type of embeddedinstrumentation entity being declared.

Embedded instrumentation entity comment 852 is inserted within the VHDLfile of the design entity that contains the finite state machine inquestion. The embedding of instrumentation for finite state machine 800is made possible by the non-conventional comment syntax illustrated inFIG. 8C and is substantially more concise than a conventional HDLinstrumentation entity suitable for accomplishing the same function.

Utilizing such embedded non-conventional comments, the system of thepresent invention creates an instrumentation entity, as described below,for instrumenting the state machine without the need to resort tocreating a separate HDL file instrumentation entity.

To support compilation and creation of embedded instrumentationentities, the previously described compilation process of FIG. 4D isenhanced as described herein. First, HDL compiler 462 is altered torecognize the presence of embedded instrumentation entity comments. If,during compilation of a design HDL file, and subject to the constraintsdescribed above for incremental compilation, HDL compiler 462 detectsone or more embedded instrumentation entity comments within the sourcecode file, HDL compiler 462 places a special marker into design entityproto data structure 463.

When instrumentation load tool 464 is passed control, proto datastructures 463 are searched in order to locate the special marker placedby HDL compiler 462 indicating embedded instrumentation entity comments.Such protos represent the design HDL files with embedded instrumentationentities that have been re-compiled in the current compilation cycle.

When instrumentation load tool 464 locates a proto data structure 463with the special marker, the corresponding VHDL source code file for thedesign entity is opened and parsed to locate the one or more embeddedinstrumentation entity comments. For each of these comments,instrumentation load tool 464 creates a specially named proto datastructure 463 a, and further generates a corresponding instance datastructure 465 a that is instantiated within the design entity. Inaddition, instrumentation load tool 464 removes the special markerinserted by HDL compiler 462 to prevent unnecessary re-instrumentationof the design proto on subsequent re-compiles.

Within these created embedded instrumentation entity protos,instrumentation load tool 464 directly creates the necessaryinstrumentation logic required by the embedded instrumentation entitywithout the need for a VHDL file to specify this instrumentation andconnects this logic to instrumentation logic block 420 of FIG. 4D. Theupdated design proto along with the embedded instrumentation entityproto and instance data structure are saved to disk and serve as inputsto subsequent compiles, removing the need to produce embeddedinstrumentation entities on subsequent recompiles.

With reference now to FIG. 9, design entity 850 is shown instrumentedwith embedded instrumentation entity 900. Embedded instrumentationentity 900 is created as a proto instantiated within design entity 850wherein the embedded non-conventional instrumentation entity commentoccurs. The embedded instrumentation entity thus may be replicatedautomatically within an overall design wherever the specific designentity is instantiated.

Embedded instrumentation entity 900 is named in a unique manner based onthe name associated with the embedded instrumentation entity by theprologue and embedded instrumentation name declaration comment. Thisname is pre-pended with a special character (shown as a “$” in FIG. 9)that is not a recognized naming convention for the platform HDL. In thismanner, the names of the embedded instrumentation entities cannotconflict with the names of any other design or standard instrumentationentities.

Furthermore, the names associated with the various events defined by theembedded instrumentation entity (the “varname” for the count events, forexample) are also derived in a fixed manner from the name associatedwith the embedded instrumentation entity. The user is required to ensurethat the names of embedded instrumentation entity events do not conflictwith the names of standard instrumentation entity events and furtherthan the names of the embedded instrumentation entities within a givendesign do not themselves conflict.

It should also be noted that if a design entity contains more than oneembedded instrumentation entity, the embedding process described withreference to FIG. 8B and FIG. 9 is simply repeated for each suchinstrumentation entity. In addition, since the protos for the embeddedinstrumentation entities are created at the same time as the designprotos itself, no changes to the BOM mechanism used for incrementalcompiles are required. The protos for the embedded instrumentationentities can be considered, for purposes of incremental compilations, tobe mere extensions to the design proto itself.

The present invention discloses a method and system for naming eventswithin a simulation model that prevents name collisions between eventsin different instrumentation entities, allows for the arbitrary re-useof components of a model in models of arbitrarily increasing size, andfurthermore allows for processing designated events in a hierarchical ornon-hierarchical manner.

When all instances of an event are considered as a whole without regardto specific instances, the event is considered in a “non-hierarchical”sense. Likewise, when an event is considered with regard to each andevery instance, it is considered in a “hierarchical” sense. Whenconsidering count events, for example, it is often convenient to trackthe number of times a particular count event occurred in the aggregatewithout concern to exactly how many times the count event occurred ineach particular instance within a simulation model.

Each type of event: count, fail, and harvest, is given a separate eventnamespace by construction. Each event class is therefore an independentgroup preventing naming collisions between the event types. The datastructure of the present invention is independently applied to each ofthe different event types to ensure correctness within each event class.

In the embodiments illustrated in FIGS. 10A, 10B, 10C, and 10D, thesystem and method of the present invention are described with respect tocount events. One skilled in the art will appreciate and understand theextensions necessary to apply the same techniques to other event classessuch as failures or harvests.

With reference to FIG. 10A, there is depicted a block diagramrepresentation of simulation model 1000 containing a number of designand instrumentation entities. As illustrated in FIG. 10A, simulationmodel 1000 includes two instances of a design entity X, with instancenames X1 and X2 respectively.

Within each of design entity instances X1 and X2 is instantiated aninstance of an instrumentation entity B3, 1012 a and 1012 b. Designentity instances X1 and X2 further comprise instances, 1014 a and 1014b, respectively, of design entity Z which further contains instances,1016 a and 1016 b, of instrumentation entity B1 and instances, 1018 aand 1018 b, of instrumentation entity B2.

Finally, simulation model 1000 includes an instance of design entity Y,with instance name Y, containing an instance of instrumentation entityB4 1022. Design entity instance Y contains an instance, 1024, of designentity Z with further instances, 1016 c and 1018 c, of instrumentationentities B1 and B2 respectively.

In what follows the methods of the present invention for uniquely namingevents will be considered in the context of exemplary model 1000. Itwill be assumed in the following description that each instrumentationentity (B1, B2, B3, and B4) has declared a single count event with eventname “count1”.

In accordance with the method and system of the present invention, theuser must uniquely name each type of event (count, fail, or harvest)within a specific instrumentation entity, i.e., the user cannot declareany two events of the same type within the same instrumentation entitywith the same event name. Such a constraint does not conflict with thestated goals of the present invention in that a given instrumentationentity is usually created by a specific person at a specific point intime, and maintaining unique names within such a limited circumstancepresents only a moderate burden to the user. The data structuredisclosed herein does, however, prevent all name collisions betweenevents in different instrumentation entities, and allows for processingthe events in a hierarchical and/or non-hierarchical manner.

As previously explained, an HDL naming convention must uniquely identifyall the entities within a given design. This constraint is inherent toHDLs and applies to design entities as well as instrumentation entities.In accordance with conventional VHDL entity naming constructs, it istechnically possible for two design entities to share the same entityname, entity_name. However, such identically-named entities must beencapsulated within a VHDL library from which a valid VHDL model may beconstructed. In such a circumstance, entity_name, as it is utilizedherein, is equivalent to the VHDL library name concatenated by a period(“.”) to the entity name as declared in the entity declaration.

Pre-pending a distinct VHDL library name to the entity namedisambiguates entities sharing the same entity name. Most HDLs include amechanism such as this for uniquely naming each design entity. Designentities must be unambiguously named in order to determine whichparticular entity is called for in any given instance in a simulationmodel. The present invention employs the prevailing naming mechanism ofthe native HDL to assign unique entity names for design entitiesthroughout a given model and leverages the uniqueness property of entitynames and the uniqueness of each instance's instantiation identifier tocreate an “extended event identifier” for each event within thesimulation model.

With reference to FIG. 10B, there is illustrated a representation of thefields in an extended event identifier data structure, alternativelyreferred to herein as an “event list”, in accordance with one embodimentof the present invention. The extended event identifier begins withinstantiation identifier field 1030. This field, as describedhereinbefore, consists of the instance identifiers, proceeding from thetop level entity to the direct ancestor of the given instance within thesimulation model separated by periods (“.”). This string is unique foreach and every instance of the event within the model. The extendedevent identifier further includes an instrumentation entity field 1032,a design entity field 1034, and an eventname field 1036.

Instrumentation entity field 1032 contains the name of theinstrumentation entity (or the name assigned to an embeddedinstrumentation entity) that generates the simulation event. Designentity field 1034 contains the entity name of the design entity in whichthe event occurs. Eventname field 1036 is the name given to the event inthe instrumentation entity description comments of an instrumentationentity or the event name assigned to an event within an embeddedinstrumentation entity. These four namespace fields comprise a uniqueidentifier for each event within a simulation model that allows for there-use of components within other models without risk of name collisionsand the consideration of events in a hierarchical or non-hierarchicalsense.

With reference now to FIG. 10C, there is shown a list of extended eventidentifiers for model 1000. Event identifiers 1040, 1041, 1042, 1043,1044, 1045, 1046, 1047, and 1048 are declared within simulation model1000 to designate count events having eventname “count1”. The extendedevent identification procedure of the present invention will bedescribed in the context of these extended event identifiers.

The uniqueness of the names in design entity name field 1034 is aprimary distinguishing factor between events. By including the designentity name in the extended event identifier, each design entity is, ineffect, given a unique namespace for the events associated with thatdesign entity, i.e., events within a given design entity cannot havename collisions with events associated with other design entities.

It is still possible however, to have name collisions between eventsdefined by different instrumentation entities that are incorporatedwithin a single design entity. Events 1041 and 1042, for example, ifidentified solely by the design entity name, have a name collision. Bothare events with eventname “count 1” within design entity Z, and iflabeled as such, are indistinguishable. In order to alleviate a namingcollision between events 1041 and 1042, the present invention employsinstrumentation entity field 1032. By referencing the design entity andinstrumentation entity names, both of which are unique with respect tothemselves and each other, a unique event namespace is created for eachinstrumentation entity associated with any given design entity. Forexample, event identifier 1041 and 1042 would be in conflict (both namedZ.count1), unless the respective instrumentation entity names areincluded within the extended event identifier to produce namesB1.Z.count1 and B2.Z.count2 for these events.

It should be noted that it is possible to uniquely name each event byusing instrumentation entity name field 1032 alone. Due to theuniqueness property of instrumentation entity names, event names thatare only named by the instrumentation entity name and the event namefield will be necessarily unique.

However, such a naming scheme is insufficient for associating eventswith a given design entity. In practice, it is desirable to associateevents with the design entity in which they occur rather thanassociating them with the potentially numerous instrumentation entitiesthat are utilized to track them. Moreover, referencing the appropriatedesign entity within the eventname allows all the events associated witha given design entity to be centrally referenced without the need toascertain the names of all the instrumentation entities associated withthe given design entity. The data structure of the present inventionutilizes both the instrumentation entity and design entity names innaming events for ease of reference at the cost of moderate uniquenessredundancy in the event names.

In an alternative embodiment of the present invention, theinstrumentation entity name is not included within the extended eventidentifier. Referring to FIG. 10D, such an alternative extended eventidentification data structure is depicted. As shown in FIG. 10D, eventsare named by instantiation identifier field 1030, design entity namefield 1034, and event name field 1036.

Such a data structure provides name collision protection between designentities but not within design entities. That is, the user must ensurethat events names for events associated with a given design entity donot collide. In case of user error in this regard, model build tools maybe utilized to detect an event name collision condition during modelcompilation. The alternative data structure depicted in FIG. 10Dprovides for simpler naming and referencing of events at the expense ofrequiring the user to prevent name collisions for events associated witha given design entity.

Returning to FIG. 10B, the combination of instrumentation entity field1032, design entity name field 1034, and eventname field 1036 for agiven event, provides a unique identifier for any given event withoutregard to multiple instantiations of the event. In order to uniquelydistinguish between multiple instantiations of an event, instantiationidentifier field 1030 is included in the extended event identifier.Instantiation identifier field 1030 field, by its construction, providesa unique string for any instance of an entity within any simulationmodel.

When evaluating occurrences of an event in a non-hierarchical sense,instantiation identifier field 1030 is ignored while searching formatching events. As illustrated in FIG. 10C, for example, anon-hierarchical query for the number of time a “count1” event occurswithin design entity Z as detected by instrumentation entity B1,utilizes the following list of count eventnames:

X1.Z B1 Z COUNT1 X2.Z B1 Z COUNT1 Y.Z B1 Z COUNT1.

These count events are added together to form an aggregate count of thetotal number of time the specific event occurred within the simulationmodel.

A hierarchical query includes specific criteria to match against thehierarchy field to limit the counter or counters found to specificinstances of the requested event. For example, a query to obtain thecount1 event of instrumentation entity B1 within the X1.Z instance ofdesign entity Z utilizes the following count eventname:

X1.Z B1 Z COUNT1,which represents the number of times the count1 event was counted byinstrumentation entity B1 within design entity instance X1.Z for aparticular simulation interval.

By providing matching model hierarchy criteria against instantiationidentifier field 1030, it is possible to consider the events withrespect to their particular instance or instances within the model,i.e., a hierarchical query. A non-hierarchical query merely ignores thehierarchy field and returns all the instances of the requested eventswithin the model.

With reference to FIG. 11A, there is depicted a block diagramillustrating a simulation model 1100 in which the hierarchical eventprocessing of the present invention is applicable. Simulation model 1100comprises atop-level design entity 1130 in which a pair of lower-leveldesign entities 1102 and 1120 is instantiated. A design entity 1104containing instrumentation entity 1106 is included within design entity1102. As illustrated in FIG. 11A, instrumentation entity 1106 includeslogic 1110 for generating a simulation event 1108 from signal set 1132from within design entity 1104. Design entity 1120 includes aninstrumentation entity 1122 that generates a simulation event 1124 usingsignal set 1134.

Utilizing the techniques described hereinbefore, generating ahierarchical event that is some logical combination of events 1108 and1124 requires the creation of an instrumentation entity associated withtop level design entity 1130 that references signal sets 1132 and 1134.Conventionally, such an instrumentation entity would substantiallyreproduce instrumentation logic 1110 and 1126 to process signal sets1132 and 1134, respectively, thus producing a copy of events 1108 and1124. Such a procedure is inefficient and prone to error. If, forexample, changes are made to any or all of signal sets 1132 and 1134, orinstrumentation logic 1110 and 1126, these changes would have to beaccurately repeated in the instrumentation entity logic for the combinedevent.

The present invention provides a mechanism whereby events, such asevents 1108 and 1124, are directly referenced and utilized as inputs tocross-hierarchical instrumentation entities. In this manner, signalconnections 1132 and 1134, as well as instrumentation logic 1110 and1126, are directly re-utilized to produce the desired hierarchicalevent.

To facilitate direct referencing of events within simulation models, aspecialized data structure is implemented within instrumentation entityinput port map comment syntax. This data structure directly connectsinput ports of instrumentation entities to cross-hierarchical eventswithin a simulation model.

For the embodiment depicted in FIG. 11A, an instrumentation entity 1150is instantiated within top-level design entity 1130 to generate ahierarchical event 1156 that is some function of events 1108 and 1124.As illustrated in FIG. 11A, instrumentation entity 1150 includes a pairof inputs 1151 and 1152 that are directly connected to events 1124 and1108, respectively, utilizing the augmented syntax described below.These input connections are logically combined using instrumentationlogic 1154 to produce a cross-hierarchical event 1156.

With reference to FIG. 11B, there is depicted a set of input portmapping comments for performing cross-hierarchical processing ofsimulation model events in accordance with the teachings of the presentinvention. In what follows, it is assumed that events 1108 and 1124 arecount events with event names event_1108 and event_1124, respectively,and that these events are connected to input ports event_1108_in andevent_1124_in on instrumentation entity 1150. As depicted in FIG. 1B, afirst input port mapping comment 1161 contains data for referencingevent 1108 to input port event_1108_in. A second input port mappingcomment 1162 contains data for referencing event 1124 to input portevent_1124_in. It should be noted that each of input port mappingcomments 1161 and 1162 includes a pre-pended non-conventional commentidentifier, --!!, that is utilized by the HDL compiler (such as compiler462 in FIG. 4D) to maintain the port mapping comments separate from thedesign.

To facilitate connection of a simulation event to an instrumentationentity input port, input port mapping comments 1161 and 1162 consist oftwo distinct parts: an instance identifier and an event identifier. Theinstance identifier is a string consisting of instance names (indescending hierarchical order) of all design entities between andincluding the design entity containing the instrumentation entity of thecross-hierarchical event being defined (i.e., the highest level designentity for the cross-hierarchical event), the design entity in which theevent that is utilized in generating the cross-hierarchical event. Ifthe design entity containing the hierarchical event is the same as thedesign entity containing the event to be connected to, the instanceidentifier is a null string. A pair of instance identifiers 1163 and1164, within input port mapping comments 1161 and 1162, respectively,specify that events 1124 and 1108 originate from signals within designentity 1120 and 1104 respectively.

Input port mapping comments 1161 and 1162 further include eventidentifiers 1165 and 1166, that identify input simulation events interms of local instrumentation entities 1106 and 1122, respectively. Inaccordance with the embodiment depicted in FIG. 11B, each eventidentifier consists of a string beginning with an open bracket (“[”)character and ending with a closed bracket (“]”) character. Betweenthese brackets, three sub-strings, delineated by period (“.”)characters, comprise a data structure utilized to identify a specificevent from which the cross-hierarchical event is defined. The firstsub-string within an event identifier is the instance name of theinstrumentation entity containing the event. The second sub-string is astring specifying the type of the event (“count”, “fail”, or “harvest”).Finally, the third sub-string is the event name of the given event asspecified in the declaration comment for the event. Each eventidentifier string uniquely identifies a single event within a givendesign entity. As depicted in FIG. 11B, event identifier strings 1165and 1166 identify events 1108 and 1124 respectively.

In accordance with an alternate embodiment of the present invention, theevent identifier naming structure is modified slightly for events thatare labeled in accordance with FIG. 10D (event names that do not includethe instrumentation entity name). When an instrumentation identifier isabsent from the extended event identifier, the event identifier stringwith an input port mapping comment consists of two sub-strings: a stringdenoting the type of event to connect to; and a string providing thename of the event separated by a period (“.”) character. Theinstrumentation entity name is not required in this case since allevents of a given type associated with a given design entity will haveunique names. The model build tools of the present invention willautomatically search all instrumentation entities associated with thedesign entity called out by the instance identifier to determine whichinstrumentation entity generates an event having the name and typeprovided in the event identifier string.

Referring to FIG. 11C, there is illustrated a set of data structures forperforming hierarchical processing of simulation model events inaccordance with a second embodiment of the present invention. In thedepicted embodiment, a pair of input port mapping comments 1171 and 1172employs a syntax compatible with the event naming data structuredepicted in FIG. 10D.

Input port mapping comment 1171 connects event 1108 to input portevent_1108_in on instrumentation entity 1150. Likewise, input portmapping comment 1172 connects event 1124 to input port event_1124_in oninstrumentation entity 1150. By utilizing the augmented syntax of FIG.11B or FIG. 11C, it is possible to create hierarchical events byconnecting the inputs of instrumentation entities to events within thesimulation model.

The above described system and method provides for practicalinstrumentation of simulation models and allows for efficientimplementation of instrumentation logic through embedded instrumentationentities. Embedded instrumentation entities, as described hereinabove,are however necessarily limited to task-specific implementations. Asdescribed with reference to FIGS. 12A and 12B, the present inventionfurther provides for a more flexible implementation of instrumentationlogic in a more unstructured manner.

It is often necessary to tailor instrumentation logic to address uniqueproblems and circumstances. Instrumentation logic of a specific and yetnon-predefined nature that is designed in accordance with the techniquesdisclosed herein with reference to FIGS. 12A and 12B is referred hereinas “random instrumentation logic.” A data construct consisting ofgeneral logic primitives (Boolean operators, storage elements, etc.) andan interconnection method for these primitives is utilized forimplementing such random instrumentation logic.

For instrumenting a simulation model as described heretofore, an HDLsuch as VHDL or Verilog is utilized as a platform from whichinstrumentation logic is generated. Appropriate instrumentation entitydescriptor comments within design entity source code files couple theresultant instrumentation entities to designated target design entitieswithin a simulation model.

In addition to entity descriptor comments within a design entity sourcecode file, the foregoing instrumentation technique requires a separateHDL file in which the instrumentation entity is described. As explainedwith reference to FIGS. 12A and 12B, the present invention provides amethod, system, and data structure for instrumenting design entitieswithin a simulation model while avoiding the design process overheadrequired for creating a separate instrumentation entity HDL file.

In accordance with the teachings of the present invention, randominstrumentation logic is directly deployed within target design entitiesin terms of individualized and customizable instrumentation descriptorcomments. Such instrumentation descriptor comments are encoded withinthe target design entity HDL source code file and provide a means forthe describing random instrumentation logic, events, andinterconnections between the created instrumentation logic and thetarget design entity. The random instrumentation logic is inserted intothe simulation model in a manner similar to the techniques used forembedded instrumentation entities to produce an instrumentation entitywithout the need for the creation of an explicit HDL instrumentationentity file.

With reference to FIG. 12A, there is illustrated a representative targetdesign entity 1200 wherein random instrumentation logic is implementedin accordance with a preferred embodiment of the present invention.Instantiated within target design entity 1200 is a design entity 1201.As further depicted in FIG. 12A, an instrumentation entity 1202 isinstantiated within design entity 1201. Instrumentation entity 1202 isdesigned in accordance with the principles set forth hereinabove togenerate a count event 1203 having an event name “count1.” Target designentity 1200 further includes an instrumentation entity 1208 that isgenerated utilizing random instrumentation logic. As depicted in FIG.12A, instrumentation entity 1208 receives as inputs signals P, A, B, andC along with count event 1203.

Instrumentation entity 1208 is constructed by a set of unconventionalcomments lines within the source code file for target design entity1200. These comments may be incorporated at any point within the logicdescription section of the HDL source code file. HDL compiler 462 (FIG.4B) recognizes the unconventional comments in addition to any commentsutilized to instantiate embedded instrumentation entities within designentity 1200. During the post-compilation/model build phase,instrumentation load tool 464 processes these comments in a mannersimilar to that utilized for embedded instrumentation entities(described with reference to FIGS. 10A-10D) to generate instrumentationentity 1208.

A variety of possible syntaxes can be utilized to formulate theunconventional HDL comments required for generating randominstrumentation logic within the source code file of a target designentity. As depicted in FIG. 12B, much of the syntax of these commentsemploys syntax similar to the concurrent subset of the VHDL languagewith the addition of syntactic and semantic enhancements that provide ameans of connection between an instrumentation entity and its targetdesign entity. In addition, minor syntactic and semantic enhancementsare provided to declare events and intermediate signals.

With reference now to FIG. 12B, there is illustrated an exemplary HDLsource code file 1220 that describes design entity 1200. Within HDLsource code file 1220, an entity instantiation 1221 produces designentity 1201, and assignment statements 1222 are utilized to generatesignals A, B, and C. A set of unconventional comments 1223 within HDLsource code file 1220 is utilized to produce instrumentation entity1208. Comments 1223 are formulated as left-hand side (l.h.s.)/right-handside (r.h.s.) assignment statements of the form:

-   -   {l.h.s.} <= {r.h.s.};        where {l.h.s.}, referred to herein after as lhs, is the        assignment statement target and, {r.h.s}, referred to herein        after as rhs is an expression denoting the logical value to be        assigned to the statement lhs. A number of rules delineate the        possible expressions for lhs and rhs in any legal statement in        the instrumentation comments.

As employed within the instrumentation data structure of the presentinvention, an lhs statement may be either an event declaration or thename of a signal that is instantiated within an instrumentation entity.An event declaration is an expression within bracket characters (“[A,A]”) that generates a new event. Within comments 1223, a statement 1230produces a count event 1240 from instrumentation entity 1208 (FIG. 12A)having eventname “countname0”.

Within an lhs event declaration, a first field designates the event type(count, fail, harvest, etc.) and is followed by such other fields as arenecessary to declare the event. As illustrated in lines 1230, 1234, and1236, such event declaration fields follow the same format as the eventdeclaration fields depicted in FIG. 4C.

Comments 1223 further include a line 1232 having an lhs that declares asignal Q within instrumentation entity 1208. To prevent ambiguity, anysignal declared in this manner may not have a name corresponding to thename of any signal present on the top level of target design entity1200. Conformance to this requirement is verified by instrumentationload tool 464 (FIG. 4D) during processing. Signals declared by an lhsexpression may be incorporated within an rhs expression as shown inlines 1232 and 1234.

An rhs consists of logical connectivity expressions and/or functionsthat combine various signals. Signals within these connectivityexpressions may originate from a number of possible sources including:signals declared on the lhs of a statement in the instrumentationcomments; signals within the target design entity; or signalsdesignating other events within the target design entity.

The absence of period (“.”) or bracket (“[”, “]”) characters within asignal value description in the rhs of a statement, designates theobject signal as corresponding to either a signal within the tophierarchical level of the target design entity or to a signal declaredon the lhs of a statement within the instrumentation language. Signalsare named in a mutually exclusive manner by the rules governing creationof signals on the lhs of a statement in the instrumentation comments,thereby preventing any ambiguity in the determining the source of thegiven signal.

Signals in rhs connectivity expressions can also be connections tosignals within entities instantiated within the target design entity. Insuch a circumstance, the instance names of the entity or entities in thehierarchy enclosing the desired signal are placed before the signal namein hierarchy order, delineated by period (“.”) characters. For example,the signal in statement 1230 (“Y.P”) represents signal 1204 withindesign entity 1201. Signals at any level of the target design hierarchyare thus accessible to instrumentation logic generated by theinstrumentation language comments.

Signals within the instrumentation comment expressions can alsodesignate other events within the target entity. Event identifiers asdescribed hereinbefore for hierarchical events are used to denote such“event” signals. For example, statement 1232 performs a logical AND ofinstrumentation event 1203 and signal A. The event identifier“Y[B1.count.count1]” connects instrumentation entity 1208 toinstrumentation event 1203. This notation permits instrumentation eventsat any level of design hierarchy within target design entity 1200 to bedirectly accessed.

As further depicted in FIG. 12B, statement 1232 produces intermediatesignal Q within instrumentation entity 1208. This is an example of aninstrumentation comment statement declaring a new intermediate signal.These signals can be used in other statements to construct randominstrumentation logic of any desired depth or complexity.

Statement 1234 utilizes intermediate signal Q along with signal 1206 toproduce fail event 1241. The syntax for fail event declaration includesa field denoting the type of event (“fail”), a field giving the eventname for the fail event (“failname0”), and a final field denoting themessage to associate with the fail. Finally, statement 1236 producesharvest event 1242.

In general, the rhs expression of any statement in the instrumentationdata structure of the present invention can access any signal orinstrumentation event signal within the target design entity utilizingthese syntactic mechanisms. These signals can be combined to form newevents or intermediate signals that can themselves be further combinedto form instrumentation logic of any desired depth or complexity.

Instrumentation comments can be placed anywhere within the logicdescription section of the target entity source code file. Allinstrumentation comments within a file are considered as a whole andproduce a single instrumentation entity within the target design entity.

It is often necessary to override signal values within a simulationmodel to test various functions and create certain conditions that wouldotherwise not be possible or simple to obtain. To provide an efficientand designer accessible means of overriding signal values within asimulation model, the present invention incorporates specialized signaloverride output ports and logic into instrumentation entities thatpermit bus signal overrides during model simulation. Such signaloverride output ports must have different names that the outputs forcount, fail, and harvest events, and compliance with this condition isverified by instrumentation load tool 464 (FIG. 4D) during the modelbuild process.

The signal override output ports may be declared explicitly as ports inthe HDL source code file of an explicitly represented instrumentationentity. For an embedded instrumentation entity or instrumentationentities produced by random instrumentation logic comments within atarget design entity, the signal override output ports are automaticallygenerated by instrumentation load tool 464. The signal override outputports are described in output port map statements that declare analternate value that overrides a given simulation signal. Such an outputport map statement further declares the conditions under which thesimulation signal will be overridden. For each simulation signal (singleor multi-bit) to be overridden, two output signals are produced: oneproviding the override signal value and another in the form of a singlebit signal that enables or disables overriding of the simulation signal.

With reference to FIG. 13A, there is depicted an exemplary HDL designentity 1300 containing a multi-bit simulation signal R. Simulationsignal R is driven by one or more sources within a logic module 1302,and is received by one or more sinks within a logic module 1304. Whilethe present invention will be described with respect to overridingsignal R, one skilled in the art will understand and appreciate theextensions necessary to apply the principles set forth herein tooverriding single bit signals or subsets of multi-bit signals.

Referring to FIG. 13B, signal override functionality is incorporatedwithin design entity 1300. As illustrated in FIG. 13B, design entity1300 includes an instrumentation entity 1306 that is equipped tooverride signal R. In the depicted embodiment, instrumentation entity1306 produces a signal R_OV(0 . . . 4) that is utilized within HDLdesign entity 1300 to selectively replace signal R as an input intologic module 1304. Instrumentation entity 1306 further produces a signalRT that enables an override of signal R with signal R_OV(0 . . . 4).

During a model build process, instrumentation load tool 464,instantiates a multiplexer (MUX) 1308 that breaks the path of theoriginal signal R as depicted in FIG. 13A to produce signal R′ that isinput into logic module 1304. MUX 1308 is directly instantiated byinstrumentation load tool 464 into the design proto data structure forinstrumentation entity 1306 without the need to alter the HDL sourcecode for HDL design entity 1300. MUX 1308 selects between overridesignal R_OV(0 . . . 4) and the original signal value R to determine thevalue of signal R′.

MUX 1308 is controlled by a select signal 1310 that is driven by logicalAND gate 1312. Logical AND gate 1312 is driven by signal RT and a latchbit signal from a latch 1314. In a manner similar to that described inrelation fail events in FIG. 5A, latch 1314, when loaded with abinary>1′ value, forces MUX 1308 to select the original signal value R,thereby disabling overrides of signal R.

Instrumentation load tool 464 generates latch 1314 and logical AND gate1312 for every overriddable signal within the simulation model. Allsignal overrides within a model can thus be selectively and individuallydisabled. For each overriddable signal, latch 1314 resides withininstrumentation logic block 420 as depicted in FIG. 4B. In accordancewith a preferred embodiment, only one instrumentation entity mayoverride a given single bit signal or any signal or subset of signalswithin a multi-bit signal.

The signal override system of the present invention further provides ameans by which instrumentation entities may access the originalunaltered version of any given signal within the simulation model. Asdepicted in FIG. 13B, for example, instrumentation entity 1306 accessesthe unaltered signal R by means of input signal 1320.

With reference to FIG. 13C, there is illustrated an exemplary HDL sourcecode file 1340 that describes instrumentation entity 1306. HDL sourcecode file 1340 includes entity descriptor comments 1351 and anarchitecture section 1358 comprising the functional logic withininstrumentation entity 1306.

Within HDL source code file 1340, an input port map statement 1364declares an input port 1309 at which instrumentation entity 1306receives signal R from logic module 1302. A set of output port mapstatements 1362 and 1363 define the output ports from instrumentationentity 1306 for signals R_OV(0 . . . 4) and RT, respectively.

An input port map comment 1360 connects signal input 1320 toinstrumentation entity 1306. Input port map comment 1360 employs anaugmented syntax from that previously described for input port mapcomments. Signals that appear within brace (“{”, “}”) characters aredefined to reference the original unaltered version of a signal within aparticular target design entity. Hence the statement

-   --!! R_IN(0 to 4) => {R(0 to 4)}    connects input port R_IN to signal R in the altered design proto    data structure. The brace syntax may be used to enclose any signal    within a port map comment or any signal referred to within a random    instrumentation comment statement RHS. If a signal referenced within    braces has no associated override mechanism in the model, the signal    declaration within braces merely resolves to the unaltered signal    itself.

An additional comment section, output declarations 1361, is furtherincluded within the descriptor comment syntax to declare signaloverrides. Output declaration comment 1361 serves to generate theoverride logic shown in FIG. 13B and connect signals RT and R_OV(0 . . .4) to the override logic. Output declarations such as output declarationcomment 1361 are of the form:

-   -   --!!<name>: out_port => target_signal [ctrl_port];        where name is a name associated with the specific signal        override (R_OVRRIDE in FIG. 13C), out_port is the output port        providing the override value for the signal, target_signal is        the name of the signal to be overridden, and ctrl_port is the        single bit output port that determines when the target signal is        overridden.

One such output declaration is required for every overriddable signal.Signals designated by out_port and ctrl_port must appear as outputsdeclared in the HDL port map for the entity. As it does forinstrumentation entities, instrumentation load tool 464 parses thecomments depicted in FIG. 13C and alters the proto data structureswithin the simulation model at model build time to produce a result suchas that depicted in FIG. 13B.

Furthermore, each signal override is given a unique name based on the“name” field within the output declaration comment in a manner analogousto that described earlier for count events. In this manner, each signaloverride can be specifically referred to for such purposes as disablingthe signal override by setting latch 1314 as shown in FIG. 13B.

While FIG. 13C illustrates only those constructs necessary forimplementing a signal override, it should be noted that there is nolimitation placed upon the creation of count, fail, and harvest eventswithin the same instrumentation entity as a signal override, andfurthermore, that multiple signal override entities may be incorporatedwithin the same instrumentation entity.

Referring to FIG. 13D, there is depicted an HDL source code file forproducing design entity 1300 wherein a set of random instrumentationcomments 1380 implement the logic necessary for selectively overridingsignal R. A comment 1381 connects the unaltered version of signal R(referred to within the braces (“{”, “}”) syntax) to an internalinstrumentation signal R_IN. A pair of comments 1382 and 1383 assignsvalues for signals R_OV and RT (the exact expressions assigned to RT andR_OV are not depicted). An event declaration instrumentation comment1384 produces a signal that enables signal R to be overridden. Inaccordance with the depicted embodiment, an event declaration comment isof the form:

-   -   [override, <name>, <target_signal>, <ctrl_signal>],        where override is a fixed string denoting a signal override,        “<name>” is the name assigned to the override, “<target_signal>”        is the name of the signal in the target entity to be altered,        and “<ctrl_signal>” is the name of the signal that determines        when the signal override takes effect. By utilizing random        instrumentation comments, a design engineer can efficiently        create signal overrides.

Since in accordance with the teachings of the present invention, signaloverrides are implemented directly using hardware constructs, it ispossible to efficiently utilize signal overrides within a hardwaresimulator.

Simulation of a given model is typically controlled by a program,hereinafter referred to as RTX (Run Time eXecutive), that is written ina high-level language such as C or C++. To facilitate RTX control andexecution of a simulation run, simulators typically provide a number ofapplication program interface (API) functions that may be called by theRTX. Such API functions employ routines, protocols, and tools that allowfor polling of signals within the simulation model, alteration ofsignals within a simulation model, cycling a simulation model, etc.

The RTX is often required to monitor occurrences of significant eventsduring model simulation. Such events typically consist of a signal or aset of signals that assume a prescribed sequence of values and will bereferred to hereinafter as “model events.” To monitor model signals, anRTX typically calls a specialized API function, hereinafter referred toas GETFAC, which allows for polling of signal values during modelsimulation.

Typically, an RTX must monitor a large number of signals, potentially ona cycle-by-cycle basis, within a simulation model and must subsequentlyprocess the signal values in order to detect the occurrence of modelevents. This approach to model event monitoring places a burden on theverification engineer in terms of re-writing RTX and communicating withdesign engineers when the signals or sequence of signal values thatconstitute a model event change.

To provide an efficient means for monitoring model events, so-called“detection events” are generated and are accessible by the RTX. Suchdetection events are generated by instrumentation entities. Detectionevents are implemented as output ports on an instrumentation entity.Furthermore, an enhanced API function is provided for directly accessingdetection events within a given simulation model.

For each detection event, a first, potentially multi-bit, output isutilized as the value of a model event. An optional second single bitsignal is utilized to indicate when the detection event occurs duringmodel simulation. By their nature, certain model events occur at eachand every cycle and therefore do not require a qualifying signal totrack their occurrences.

With reference to FIG. 14A, there is illustrated a block diagramdepicting data content within main memory 44 (FIG. 2) during asimulation run of a simulation model 1400. Main memory 44 includes theelements necessary for monitoring an exemplary model event including asoftware simulator 1410 that simulates simulation model 1400 under thecontrol of an RTX 1405.

RTX 1405 delivers a set of API calls 1430 to API function GETFAC withinsimulator 1410 to obtain the values of signals A, B, C, and D withinmodel 1400. Further processing of these signal events is performedutilizing an RTX code 1450 culminating in the assignment of the modelevent value to variable event_x at RTX code line 1455.

Referring to FIG. 14B, there is illustrated a block diagram depictingcontents of main memory 44 during a simulation run in accordance with apreferred embodiment of the present invention. In the depictedembodiment, an instrumentation entity 1460 is instantiated withinsimulation model 1400 using techniques described above. Instrumentationentity 1460 directly monitors signals A, B, C, and D by means of a setof signal connections 1462. Signal connections 1462 provide a moreefficient means to monitor signals than GETFAC API function calls.

Within instrumentation entity 1460, instrumentation logic 1464substantially recreates the function of RTX code 1450 of FIG. 14A andproduces signals 1466 and 1468, which denote the value of a model eventand when the model event occurs, respectively.

Each detection event within a given simulation model is assigned aunique name in a manner described below. During model build,instrumentation load tool 464 (FIG. 4D) generates a data structure inthe form of a table within the simulation model that uniquely names allthe detection events within a given simulation model and thecorresponding instrumentation entity output signals. This table will bereferred to hereinafter as the detection event translation table.

An API function GETEVENT( ) is provided within software simulator 1410for accessing model detection events. API function GETEVENT references adetection event translation table 1470 to locate signals 1466 and 1468in response to a call 1472 by RTX to obtain the value of model eventevent_x. RTX 1405 obtains the value of model event event_x withoutdelivering a number of GETFAC API calls, and furthermore, without theneed to process the signal values associated with the model event. TheRTX code is thus insulated from potential changes to the signals andsignal sequence values defining model event event_x. Any changes to thedetailed definition of model event event_x are reflected withininstrumentation entity 1460 and no changes to the RTX are necessary.

With reference to FIG. 14C, there is illustrated an exemplary HDL sourcecode file 1480 that describes instrumentation entity 1460 in accordancewith a preferred embodiment of the present invention. As shown in FIG.14C, exemplary file 1480 consists of a number of entity descriptorcomments 1491 and an architecture section 1492 comprisinginstrumentation logic 1464.

Within HDL file 1480, a set of input port map comments 1493 serve togenerate connections 1462 of FIG. 14B. An additional comment section,detection declarations 1494 is incorporated within the entity descriptorcomment syntax that allows for declaring detection events. A detectiondeclaration comment 1495 serves to generate and uniquely name detectionevent event_x. Moreover, detection declaration comment 1495 associatessignals 1466 and 1468 of FIG. 14B with event_x. Detection eventdeclarations, such as detection event declaration 1495 are of the form:

-   -   --!!<name>: event_value_port [ctrl_signal];        where name is a name associated with the specific detection        event (event_x in FIG. 14C), event_value_ort is the output port        providing the value for the detection event, and ctrl_signal is        an optional single bit output port that flags an occurrence of        the model event.

Each detection event is uniquely named in accordance with the name fieldwithin the output declaration comment in a manner analogous to thatdescribed earlier for count events. Such detection event names, togetherwith the corresponding instrumentation entity output ports, are insertedinto the detection event translation table data structure that is placedwithin the model by instrumentation load tool 464. API function GETEVENTreceives the extended event identifier associated with a given event asan input and returns the model event value and, if applicable, anindication of whether the event occurred in the current cycle.

While FIG. 14C illustrates only those constructs necessary forimplementing a detection event, the principles set forth herein place nolimitation on the generation of count, fail, and harvest events orsignal overrides within the same instrumentation entity as a detectionevent. Moreover, multiple detection events may be incorporated withinthe same instrumentation entity.

Within the spirit and scope of the present invention, detection eventsmay be created within random instrumentation comments in a mannerlargely similar to that described with reference to signal overrides.Detection events can also be combined, in a manner similar to that shownearlier, as part of a hierarchical event.

Finally, it should be noted that the present invention may be practicedin conjunction with a hardware simulator. As for software simulators,hardware simulators are controlled by an RTX program. To adapt theprinciples set forth herein to a hardware simulator environment, thehardware simulator provides a GETEVENT API function and accepts modelscontaining a detection event translation table.

By utilizing random instrumentation comments, a design engineer canefficiently create representations of model events accessible to RTX.Such representations need not change even if the detailed definition ofthe model event changes. Such stability reduces the complexity andburden of maintaining RTX and lowers the amount of communicationrequired between design and verification engineers.

In order to provide for the control and monitoring of instrumentationevents within simulation models executing on a batch simulation farm,one or more general-purpose computers, hereinafter referred to as“instrumentation servers”, are added to batch simulation farms. Aninstrumentation server acts as a centralized repository for informationused to control instrumentation events and for data gathered frominstrumentation events during simulation runs. The exact nature andfunction of the control information and of the gathered data varies withthe type of event (i.e., fail events vs. count events), as will bedescribed below.

In order to allow for effective management of instrumentation events, aset of “eventlist” files (described with reference to FIGS. 10A-10D)contain information about the exact number and content of theinstrumentation events in a given model. The eventlist files are createdat model build time by instrumentation load tool 464. These files, oneper class of events (fail, count, harvest, etc.), list the particularevents in the given simulation model. Each simulation model has a uniqueset of corresponding eventlist files that are created at model buildtime.

When instrumentation events are created at model build time, they areconstructed in a specific order, and a unique index is assigned withinthe eventlist file to each instrumentation event for a given eventclass. Accesses to instrumentation events by API routines make use ofthese index values. Furthermore, when an API routine communicatesaggregate data with respect to all events within a given event class tothe instrumentation server, this aggregate data is sequentially orderedaccording to these index values.

Each eventlist file contains a list of the instrumentation events for agiven event class within the model. These events are named in accordancewith the naming convention data structures described above inconjunction with FIGS. 10A-10C, which provides unique names for each ofthe instrumentation events. Referring back to FIG. 10A in conjunctionwith FIG. 15, there is shown an eventlist file 1660 for the count eventsof simulation model 1000 of FIG. 10A.

Eventlist file 1660 contains multiple count event class entries 1663.Each of count event class entries 1663 includes a unique, sequentialindex value 1661, and an extended event identifier 1662. Each of indices1661 corresponds to the index of a particular event (in this case countevent COUNT1) assigned at model build time. Extended event identifiers1662 provide an event name associated with each individual event index.Eventlist file 1660 thus provides a mapping between the instrumentationevent names and the instrumentation event indices as well as providingan ordering convention for aggregate data for a class of instrumentationevents. Eventlist files, such as eventlist file 1660, are used by theinstrumentation server to aid in the control and monitoring ofinstrumentation events in simulation models.

With reference now to FIG. 16A, there is illustrated a batch simulationfarm 1601 in which a preferred embodiment of the present invention maybe implemented. Batch simulation farm 1601 consists of geographicallydistant simulation farm nodes 1680 a-d. Within these nodes,general-purpose computers 1600 a-n are interconnected via local areanetworks (LANs) 1610 a-1610 d. LANs 1610 a-1610 d are further connectedby means of a wide-area network (WAN) 1690, which provides communicationamong multiple simulation farm nodes 1680 a-d. Those skilled in the artwill recognize that many possible network topologies are possible for abatch simulation farm.

One such general-purpose computer 1607, together with a set of diskstorage devices 1609 serve as a shared file system, which is accessibleto all general-purpose computers within simulation farm nodes 1680 a-d.Exemplary batch simulation farm 1601 has been shown with one shared filesystem server in a particular geographic node. Those skilled in the artwill recognize that it is possible for the shared file system to beimplemented as multiple general-purpose computers and disk devicesacross the different geographic nodes in the batch simulation farm.Further, it is possible for each distinct geographic node to have aunique shared file system. Such unique file systems are usuallyaccessible to all nodes, but are most advantageously accessed within thelocal network node wherein the file system resides.

Within simulation farm node 1680 a, a particular general-purposecomputer serves as an instrumentation server 1699. Although a singleinstrumentation server is described with reference to the batchsimulation farm environment shown in the figures, those skilled in theart will understand the extensions necessary to distribute thefunctionality of the instrumentation server across several physicalgeneral-purpose computers.

General-purpose computers 1600 a-n within simulation farm nodes 1680 a-dutilize software or hardware simulators to perform simulation tests onvarious digital design simulation models. In addition, certaindesignated general-purpose computers 1600 within batch simulation farm1601 serve specific roles in the process of controlling and executingsimulation tests as described below. Many of general-purpose computers1600 may also be user machines that execute simulation tests as asecondary background function.

At any given time, a number of distinct versions of a simulation modelfor a given digital logic design may be undergoing simulation withinbatch simulation farm 1601. In addition, a number of different digitaldesigns, each with their respective differing model versions, may beundergoing simulation. In such circumstances, each different model istypically given a name, hereinafter referred to as the “model name”,which uniquely identifies both the particular digital design and theparticular version of the simulation model for the given digital design.

One or more general-purpose computers 1600, hereinafter referred to as“model servers”, are utilized to store the valid versions of simulationmodels currently available for execution within batch simulation farm1601. Before simulation jobs can be executed within batch simulationfarm 1601 with respect to a particular model, that model must be built,and a copy of the model placed on the model servers. In addition, theeventlist files for the model must be placed on instrumentation server1699 to allow for the control and monitoring of the instrumentationevents.

Within batch simulation farm 1601, one or more of general-purposecomputers 1600 a-n, referred to hereinafter as “testcase generators”,are typically utilized to create simulation testcases for the variousmodels under simulation. The testcase generators are responsible forgenerating tests to be executed and further packaging these tests intosimulation jobs. A simulation job is an entity containing the simulationtest and any controlling information and/or programs (such as RTX) thatare necessary to execute the simulation test within batch simulationfarm 1601.

Simulation jobs are passed from the testcase generators to one or moreof general-purpose computers 1600 a-n that are utilized as batchcontrollers, within batch simulation farm 1601. These batch controllersare responsible for dispatching simulation jobs to a general-purposecomputer utilized as a simulation platform, herein after referred to asa “simulation client”.

Once a simulation job arrives at a simulation client, the simulationclient communicates with the model servers to obtain a copy of thesimulation model corresponding to the particular simulation job. Themodel can be transferred to the simulation client by a number of meanswell known to those skilled in the art including, among others, a sharedfile system, File Transfer Protocol (FTP), or a custom file transportmechanism utilizing network communication protocols.

In addition, the simulation client communicates with instrumentationserver 1699, the shared file system comprising general-purpose computer1607 and disk storage devices 1609, or some combination thereof, inorder to obtain the control information for the instrumentation eventswithin the model. This control information is stored on a per modelbasis by model name on instrumentation server 1699. The exact contentsand nature of the communication between the simulation client andinstrumentation server 1699 varies with the type of events within themodel as explained in further detail below. The instrumentation eventcontrol information is used by API routines called by RTX to control thebehavior of the instrumentation events within the simulation model.

The simulation model is then loaded into either memory 44 or thehardware simulator within the simulation client. Model processingcontrol is then passed to RTX for the execution of the simulationtestcase. RTX executes the testcase until the successful completion ofthe test or an error condition (test fail) occurs.

Within batch simulation farm 1601, one or more of general-purposecomputers 1600 a-n, hereinafter referred to as “statistics servers”, areutilized to store general statistics, such as cycles completed, numberof passing tests executed, etc. concerning the execution of simulationjobs within batch simulation farm 1601. Likewise, one or more ofgeneral-purpose computers 1600 a-n, hereinafter referred to as “failedtestcase servers”, are utilized to store simulation tests that havefailed in order to facilitate the re-execution and debugging of thesetestcases.

At the conclusion of the execution of a testcase, whether due tosuccessful execution or a failure, RTX communicates with the statisticsserver to record general statistics about the execution of thesimulation job. Such communication can be accomplished in a number ofways well known to those skilled in the art including a shared filesystem, a direct network connection between RTX and the statisticsserver, a file transport mechanism, and others.

At the conclusion of a testcase, RTX also communicates the aggregateinformation concerning instrumentation events to instrumentation server1699. This information is stored on instrumentation server 1699 forfuture analysis and in some cases is utilized to control instrumentationevents in future simulation testcase runs for a given model. The exactnature of this communication varies for the different event classes asexplained in further detail below.

If a testcase concludes due to a failure, RTX communicates with thefailed testcase server to save those elements of the simulation jobrequired to allow for the reproduction of the failed simulationtestcase. The failed testcase server serves as a repository of failedtests that may be retrieved and re-executed in a foreground manner toallow for detailed investigation and problem resolution.

It is important to note that different simulation models typicallyrequire differing forms of testcases. What constitutes a testcasevaries, often dramatically, between different simulation models. This isdue to the varied techniques utilized in the present art for simulationof digital systems. In such circumstances, the failed testcase serversprovide mechanisms capable of storing each of the various differentforms of testcases.

In response to RTX communicating the general statistics for a simulationjob to the statistics servers, communicating the aggregate statisticsfor the instrumentation events to instrumentation server 1699, andarranging for the storage of any failed simulation testcases on thefailed testcase servers, RTX terminates and the simulation client isreleased. The batch controllers can then dispatch a new simulation jobto the simulation client for execution. Those skilled in the art willrecognize that many potential variations in the operation of a batchsimulation farm are possible.

With reference to the flowchart of FIG. 16B in conjunction with FIG. 15,there is depicted a progression of events from the creation of aspecific simulation model to the removal of that model from batchsimulation farm 1601 and instrumentation server 1699. The process beginsat step 1621, which depicts the creation of the given simulation model.The simulation model is created in accordance with model build processesdescribed hereinbefore.

Proceeding to step 1622, the model is placed on the model server to beavailable for simulation jobs executed within batch simulation farm1601. Next, as illustrated at step 1655, the model eventlist files areplaced on instrumentation server 1699. Once the eventlist files for agiven model are placed on instrumentation server 1699, instrumentationserver 1699 begins controlling instrumentation events and gatheringinstrumentation event data for the given model. Placing the eventlistfiles on instrumentation server 1699 will be referred to hereinafter as“commissioning” a model.

The process continues as depicted at step 1623, with a determination ofwhether all the desired testing for the given model has been completed.If, as illustrated at step 1625, not all testing for the given model iscomplete, a new testcase is generated by the testcase generators.Subsequent to generation of a new testcase, a batch controllerdispatches the resultant simulation job to a simulation client forexecution as shown at step 1626. The simulation job is then executed onthe simulation client as depicted at step 1627. Finally, the processreturns to step 1623 to repeat until model testing for the given modelis complete and the model is removed from the batch simulation farm asillustrated at step 1624.

Those skilled in the art will recognize that it is possible for severalconcurrent simulation jobs for the same model to be executingcontemporaneously within batch simulation farm 1601. That is to say,steps 1625-1627 may be executed with respect to the same model a numberof times concurrently by batch controllers within batch simulation farm1601. The given simulation model is not removed from batch simulationfarm 1601 until all outstanding jobs, potentially executingconcurrently, for the given simulation model have completed execution.Referring to step 1624, when all testing for the model has beencompleted, the model is removed from the model servers and thereforefrom batch simulation farm 1601.

It is often necessary to access particular elements of theinstrumentation data for a particular model even after the model hasbeen removed from batch simulation farm 1601. Process step 1628 depictsa determination of whether there still exists a need for access to theinstrumentation data stored within instrumentation server 1699 for thegiven model. In response to a determination that all necessary access toinstrumentation data for the model has been completed, the processcontinues as shown at step 1629, with the eventlist files, controlinformation, and instrumentation data files for the given model allbeing removed from instrumentation server 1699, thereby removing themodel in its entirety from instrumentation server 1699.

With reference to the flowchart of FIG. 16C, the steps involved insimulation job execution step 1627 of FIG. 16C are depicted in greaterdetail. The process of executing a simulation job on a simulation clientbegins with step 1631, which depicts the simulation client obtaining acopy of the model corresponding to the given simulation job provided bythe model servers. As illustrated at step 1638, the simulation clientcommunicates with instrumentation server 1699 to obtain and processcontrol information for the instrumentation events within the simulationmodel. Proceeding to step 1632, the simulation model is loaded into ahardware simulator or memory 44 of the simulation client.

The process then moves to step 1633, which depicts the execution of thesimulation test under RTX control. Once the simulation test is complete,and as illustrated at step 1634, RTX delivers the aggregate statisticalresults data obtained from the simulation job to the statistics serverwherein the data are logged. Next, as depicted at step 1635, adetermination is made of whether or not the testcase failed. If thetestcase has failed, and as shown at step 1636, the RTX communicates thefailed testcase to the failed testcase servers. If it is determined atstep 1635 that the testcase did not fail, the process continues at step1637, which depicts RTX delivering various aggregate instrumentationevent data to the instrumentation server 1699 as will be describedbelow. The process concludes with step 1639, illustrating the simulationclient being released to perform other simulation jobs.

In an environment such as batch simulation farm 1601, which contains apotentially large number of different versions of active simulationmodels, it is important to ensure the correctness of the aggregateinstrumentation data communicated from the simulation clients to theinstrumentation server for the various models. Each simulation modelcontains a unique set of instrumentation events that are ordered, perclass of events, in a unique fashion. The nature and identity of thesesets of events are delivered to instrumentation server 1699 when theeventlist files for each model are commissioned onto instrumentationserver 1699.

Aggregate data about a group of instrumentation events delivered from asimulation client are identified by the model name withininstrumentation server 1699. Once a particular simulation model iscommissioned (i.e., the eventlist files for the model have been placedon instrumentation server 1699), it may be useful to enableinstrumentation server 1699 to determine if the aggregateinstrumentation data received under a given model name correctlycorresponds to the instrumentation data for the model named inaccordance with the model name originally commissioned oninstrumentation server 1699.

Simulation clients 1600 a-n may also be utilized to simulate a modeldirectly in a foreground mode without using the batch simulation farm.Such a foreground simulation does not require the placement of a modelon the model servers or the placement of the eventlist files on theinstrumentation server. Such independent model processing is onepossibility necessitating a method for ensuring consistency of eventlistmodel data within instrumentation server 1699. To provide a means touniquely correlate aggregate instrumentation data to a specificsimulation model, instrumentation load tool 464 places a number ofso-called “digital signatures” within each simulation model. Thesesignatures are computed for each instrumentation event class and aresubsequently utilized by a cyclic-redundancy-check (CRC) check functionto ensure an exact correspondence between models commissioned withininstrumentation server 1699 and subsequently arriving model eventinformation. As explained in further detail with reference to FIGS.17A-C, a digital signature is generated that uniquely corresponds to theeventlist contents for each event class. The digital signature iscomputed from the eventlist data for a given set of events within aspecific simulation model. Any alterations, additions, or deletions toan eventlist file will result in a differing digital signature.

The use of CRC values is well known to those skilled in the art and theyare used in a variety of other circumstances to detect corruption ofdata during transmission or storage. As implemented herein, however, theCRC “digital signature” serves not to detect errors in the physicaltransmission or storage of data, but rather as a unique signature forthe contents of an eventlist file for a simulation model. For example,if a model with a different set of instrumentation events is created,but given the same name as an earlier model, the contents of theeventlists are changed and therefore the value of the CRC digitalsignatures will differ from those of the original model.

Referring to FIG. 17A, there is illustrated a block diagram depictingdata content within main memory 44 (FIG. 2), including a simulationclient 1701, during a simulation run of a simulation model 1700 inaccordance with a preferred embodiment of the present invention. Withinsimulation model 1700, digital signatures 1710 a-n correspond to a CRCvalue calculated by instrumentation load tool 464 for the variouseventlists describing all instrumentation events contained in simulationmodel 1700.

At the conclusion of a simulation run, an RTX 1702 communicatesaggregate instrumentation event data to instrumentation server 1699, asdepicted in step 1637 of FIG. 16C. To communicate the aggregate eventinstrumentation data, RTX 1702 calls API entry point 1740 within asimulator 1735. API entry point routine 1740 collects theinstrumentation event data into an “aggregate data packet” (depicted inFIG. 17B), which is delivered to instrumentation server 1699 through anetwork interface 1720. Distinct API entry points are provided for eachclass of instrumentation events that must communicate aggregate datawith instrumentation server 1699.

With reference to FIG. 17B, an aggregate data packet 1750, such as thatdelivered by API entry point routine 1740 to instrumentation server1699, is depicted. Aggregate data packet 1750 contains a model namefield 1751, a CRC digital signature value 1752, and a data field 1753.Model name field 1751 consists of the name of simulation model 1700. CRCdigital signature value 1752 contains the digital signature value forthe class of events communicated in aggregate data packet 1750. Datafield 1753 contains the aggregate instrumentation event data for model1700. The nature and contents of this data varies for each class ofinstrumentation events.

Referring to FIG. 17C, there is shown a process by which model eventdata for aggregate data packets received by instrumentation server 1699is validated in accordance with a preferred embodiment of the presentinvention. The process begins at step 1770 with instrumentation server1699 receiving an eventlist for a specific model during modelcommissioning. Next, as illustrated at step 1772, instrumentation server1699 computes and stores a CRC digital signature uniquely characterizedby the contents of the eventlist. Instrumentation server 1699 employsthe same CRC computation function as that utilized by instrumentationload tool 464 to generate the CRC digital signature value such as CRCvalue 1752.

Proceeding to step 1774, instrumentation server 1699 receives anaggregate data packet structured as depicted in FIG. 17B from simulationclient 1701. It should be noted that the CRC value contained with theaggregate packet received at step 1174 was previously computed andstored by instrumentation load tool 464. The process continues asdepicted at step 1776, with a determination of whether or not theaggregate data packet corresponds to a model and event class that haspreviously been commissioned with instrumentation server 1699. If not,and as illustrated at step 1782, the packet is discarded. If, however,the aggregate data packet corresponds to a class of event for a modelname commissioned on instrumentation server 1699, the process proceedsto step 1778.

Step 1778 depicts a determination of whether or not the CRC digitalsignature contained within aggregate data packet 1750 matches the CRCvalue computed and stored at step 1772. If the CRC check values match,and as illustrated at step 1780, the packet is processed. The exactnature of the processing varies with the type of packet as explained infurther detail below. If the CRC check values do not match, the packetis discarded as depicted at step 1782. Following either packetprocessing (step 1780) or packet discard (step 1782), the processreturns to step 1774 at which a next arriving aggregate data packet isreceived.

By calculating and storing a CRC digital signature value for eacheventlist received when models are commissioned, instrumentation server1699 can verify that each aggregate data packet received for a givenmodel name matches the data contents of the model having that name whenoriginally commissioned on the instrumentation server. In this manner,instrumentation server 1699 can ensure that the data receivedcorresponds to the model (as defined by the model name) as that modelwas defined at the time it was commissioned.

In the context of a batch simulation farm, it is advantageous to providea means to centrally disable instrumentation events without the need torecompile or redistribute simulation models. Such a mechanism isparticularly useful for centrally disabling a faulty or undesired failinstrumentation event within a simulation model that may be simulatedwithin any number of simulation clients within the batch simulationfarm.

Faulty fail instrumentation events may result in a large number oftestcases being erroneously reported as failing and subsequently beingerroneously stored for analysis. Although the following description ofcentralized instrumentation event disablement is explained only for failevents, one skilled in the art will appreciate that similar techniquescould be applied to instrumentation event types other than fail events.

FIG. 18A illustrates contents of memory 44 within simulation client 1701during execution of a simulation job. Prior to execution of thesimulation job, RTX 1702, as part of step 1638 of FIG. 16C, calls APIentry point 1800, disable_events( ). API entry point 1800 is a routinethat communicates with instrumentation server 1699 and/or shared filesystem 1609 to obtain a list of events to be disabled, hereafterreferred to as a “fail disable list”. Separate, initially empty faildisable lists are stored for each active model within batch simulationfarm 1601.

Once the fail disable list is obtained by RTX 1702, API entry point 1800further calls API entry point 1802, set_fail_mask( ), to disable thespecific failure events listed in the retrieved fail disable list. Todisable, or mask, the fail events specified in the fail disable list,API entry point 1802 sets appropriate fail mask registers 507 a-n asdescribed with reference to FIG. 5A.

API entry point 1800 obtains the fail disable list from one of twosources. The first source is a master file 1805 stored on disk ininstrumentation server 1699. The second possible source is from anauxiliary file 1807 stored in association with general purpose computer1607 in shared file system 1609. Master file 1805 serves as the primarycopy of the fail disable list. At regular intervals, instrumentationserver 1699 copies the contents of master file 1805 into auxiliary file1807, which serves as a backup copy of the fail disable list.

The two-file system depicted in FIG. 18A is used to maintain the faildisable list in a flexible and robust manner within geographicallydistributed simulation farm 1601. Simulation clients that obtain thefail disable list from shared file system 1609 can potentially receive a“stale” copy of the disable fail list. In practice, this may not pose aproblem since master file 1805 is copied onto auxiliary file 1807 on aregular interval, and any discrepancies between master file 1805 andauxiliary file 1807 are quickly reconciled.

In one implementation, simulation clients within the same local areanetwork as instrumentation server 1699 are configured to primarilyobtain the fail disable list by direct communication withinstrumentation server 1699. In response to a communication failure withinstrumentation server 1699, the simulation clients would attempt toobtain the fail disable list from shared file system 1609, which mayreside within or outside the requestor's local area.

A direct network connection with a geographically remote instrumentationserver can potentially be more error prone and have lower performancethan a local connection to a geographically local shared file system.Therefore, simulation clients that are geographically remote (i.e., notin the same local area network) with respect to instrumentation server1699, may be configured to initially attempt to obtain the disable faillist from a local shared file system. In alternate implementations,simulation clients may be configured to obtain the fail disable listfrom shared file system 1609 in order to reduce network traffic toinstrumentation server 1699. By having two separate sources for the faildisable list, the simulation clients can be configured to balance thetraffic between instrumentation server 1699 and shared file systems1609.

Referring to the flowchart of FIG. 18B, wherein is depicted theprocesses performed with respect to API entry point 1800 in greaterdetail. As shown in FIG. 18B, the process begins with a fail listrequest by RTX 1702 as depicted at step 1829. In response to the RTXrequest, and as illustrated at step 1830, a determination is made ofwhether or not an attempt should be made to access instrumentationserver 1699 to obtain the disable failure list. If a determination ismade not to attempt to access the fail disable list from instrumentationserver 1699, the process continues at step 1836, with a furtherdetermination of whether or not to access shared file system 1609.Otherwise, as illustrated at step 1832, simulation client 1701 attemptsto obtain the fail disable list from instrumentation server 1699.

Proceeding to step 1834, if the attempt to access instrumentation server1699 was successful, the fail events specified in the fail disable listare disabled via API entry points 1800 and 1802 as illustrated at step1844. In the case of an unsuccessful attempt to access instrumentationserver 1699, and as depicted at step 1836, a further determination ismade of whether to attempt to access shared file system 1609 in analternative attempt to obtain the fail disable list. As depicted atsteps 1838, 1840, and 1844, the fail events specified in the faildisable list are disabled (i.e., API entry point 1800 calls API entrypoint 1802 to disable the designated failure events) in response tosimulation client 1701 successfully accessing shared file system 1609.The failure event disablement process concludes step 1846, depicting APIentry point returning a successful disablement indication to RTX 1702.If the attempt to access shared file system 1609 is unsuccessful, theprocess concludes with step 1842, illustrating API call 1800 returningan indication to RTX 1702 that the attempt to mask the failure eventsfailed.

To initiate disablement of one or more fail events for a givensimulation model, a user adds an entry to a fail disable list associatedwith the simulation model within master file 1805. Subsequently,simulation clients utilizing the fail disable list within master file1805 will disable the fail event(s) for the particular simulation modelin accordance with the process illustrated in FIG. 18B. At apre-determined interval, the fail disable list within master file 1805is delivered to replace the failure disable list within auxiliary file1807, and the currently active simulation clients will disable thefailure event(s) specified in the updated list for the specified model.

Within a fail disable list file, fail events are specified by entriescorresponding in structure to the event identifiers for failure eventswith possible wildcard extensions in the various eventname fields. Suchwildcard extensions permit, for example, the automatic disablement ofall the replicated instances of a given failure event without having toexplicitly list all the instances of the failure event. However, byutilizing entries without wildcards within the failure disable file, thefailure disable list provides the ability to selectively disablespecific individual failure event instances as well.

FIGS. 18A and 18B illustrate a user-initiated mechanism for centrallydisabling fail events within a batch simulation farm environment.Typically, batch simulation farms run continuously and cannot bedirectly monitored at all times. As will be explained with reference toFIGS. 19A and 19B, the present invention provides an autonomous means ofdisabling failure events that does not require active user intervention.

With reference to FIG. 19A, there are depicted contents of memory 44within simulation client 1701 at the conclusion of a simulation job inaccordance with a preferred embodiment of the present invention.Simulation model 1700 contains fail events identified within fail eventflag registers 500 a-n as described hereinbefore in conjunction withFIG. 5A. Signal 511, driven by logical OR gate 512, indicates theoccurrence of a failure event during a simulation job.

As part of step 1637 of FIG. 16C, RTX 1702 calls an API entry point1900, report_fails( ). API entry point 1900 first examines signal 511 todetermine if any of the fail events specified within flag registers 500a-n have occurred during a simulation run. If none of the specifiedfailure events has occurred, API entry point 1900 terminates furtheraction.

However, if one or more of the specified failure events have occurredduring the simulation run, API entry point 1900 generates and delivers acorresponding aggregate data packet for the occurring failure events vianetwork interface 1720 to instrumentation server 1699. The contents ofregisters 500 a-n are contained, for example, in the data field of theaggregate data packet delivered to instrumentation server 1699. In thismanner, instrumentation server 1699 receives information for everyfailure event that occurs within batch simulation farm 1601. Inaccordance with the depicted embodiment, instrumentation server 1699maintains a set of counters 1901, one set per commissioned model, tomonitor the rate of occurrence for individual failure events.

After verifying the correctness of the fail event aggregate data packet,in accordance with the process described above with reference to FIG.17C, instrumentation server 1699 increments counters 1901 to record theoccurrence of the failure events in model 1700. Contemporaneously withprocessing aggregate failure event data packets, instrumentation server1699 decrements counters 1901 at a regular interval. In this manner,counters 1901 indicate the number of times a failure event has occurredwithin a given time interval rather than the total number of times afailure event has occurred. The interval at which counters 1901 aredecremented is a predetermined value that can be adjusted.

Within counters 1901, only one counter is provided per specific failevent without regard to the differing instances of the specific failevent. That is to say, failure events are considered in anon-hierarchical sense as described above. Occurrences of fail eventsfrom all instances of a given fail event are added into a singlecounter. Each of counters 1901 therefore represents the rate at which agiven failure event occurs, irrespective of which instance or instancesof the failure event occur. In practice, it is preferable to considerfailure events in a non-hierarchical sense, since, in general, thesignificance of a given failure event does not depend on where insimulation model 1700 the failure occurs.

Referring to FIG. 19B, there is depicted a flow diagram of a process bywhich instrumentation server 1699 processes fail event aggregate datapackets in accordance with a preferred embodiment of the presentinvention. The process begins at step 1910 and proceeds to step 1912,which depicts instrumentation server 1699 receiving a fail eventaggregate data packet. The process continues with step 1914,illustrating a determination of whether or not the aggregate data packetcorresponds to a model commissioned within instrumentation server 1699in accordance with the digital signature verification method explainedwith reference to FIGS. 17A-C. If the aggregate data packet cannot beverified, it is discarded as illustrated at step 1916.

Otherwise, if the aggregate data packet has been verified ascorresponding to a commissioned model, counters 1901 are incremented inaccordance with packet data content within instrumentation server 1699as depicted at step 1918. The fail threshold process of the depictedembodiment is based on rates of occurrences of fail events rather than acumulative evaluation. To this end, and as depicted at steps 1925 and1927, all of counters 1901 are decremented at a predetermined timeinterval during processing of received aggregate fail event packets.

Following counter incrementation, and as illustrated at step 1920, adetermination is made of whether or not any of counters 1901 hasexceeded a predetermined threshold. Any counter having exceeded thisthreshold indicates that a given fail event is occurring at toofrequently (i.e., at an excessive rate). Responsive to a determinationthat any of counters 1901 has exceeded its threshold, the processcontinues as illustrated at step 1922, with instrumentation server 1699adding an entry for the excessively occurring fail event into the faildisable list within master file 1805. This entry disables all instancesof the problematic failure event within simulation model 1700, and withrespect to the disabled failure event, the process terminates as shownat step 1924.

If, as determined at step 1920, no counters have exceeded the threshold,the fail threshold process terminates as shown at step 1924 with respectto the packet received at step 1912. Instrumentation server 1699 repeatsthe steps depicted in FIG. 19B for each fail event aggregate data packetreceived from API entry point 1900, report_fails( ).

The process illustrated in FIG. 19B provides a means by whichinstrumentation server 1699 monitors the occurrence of failure eventswithin simulation models executed on batch simulation farm 1601. When agiven failure event occurs faster than a certain threshold,instrumentation server 1699 automatically disables the failure event.This process occurs without the need for user intervention.

In an environment such as batch simulation farm 1601, it is common for agiven design entity to be utilized in a number of different models ofvarying complexity and size. A given design entity may appear indifferent models ranging in complexity from a model containing a subsetof the integrated circuit in which the entity resides to a modelcontaining an entire system with potentially multiple instances of thephysical chip in which the design entity resides. Furthermore, there maybe several versions of each of these different models active withinbatch simulation farm 1601 at any given time.

In such an environment, it advantageous to provide a means that allowsan HDL circuit designer to access count event data for a given designentity without requiring specific knowledge of which active models inthe batch simulation farm contain that design entity. In practice,designers are generally not aware of the specifics of the models activein a batch simulation farm at any given time.

In addition, once a designer registers a request for count event data,it is useful if this request can be repeated without user interventionat specified intervals, and that the counter data are returnedautomatically to the designer's workstation for on-going evaluation. Inthe following description, a request (from an HDL designer, for example)for counter data submitted within a batch simulation farm environmentwill be referred to as a “counter query”. Counter queries are deliveredfrom one of general-purpose computers 1600 to instrumentation server1699 for storage and processing. In accordance with the embodimentsdescribed herein, a separate list of counter queries is maintained foreach individual user.

With reference to FIG. 20A, there is depicted the contents of memory 44at the conclusion of a simulation processing job performed with respectto simulation model 1700 within simulation client 1701. Simulation model1700 contains count event registers 421 a-421 n as describedhereinbefore with reference to FIG. 4B. Each of count event registers421 a-421 n maintains a count representing the number of times aparticular instrumentation count event has occurred during thesimulation of simulation model 1700.

As part of step 1637 of FIG. 16C, RTX 1702 calls an API entry pointrpt_counts( ) 2000. API entry point 2000 generates and delivers anaggregate data packet containing the results registered in count eventregisters 421 a-421 n to instrumentation server 1699 via network 1720.Upon receipt of the aggregate count event data packet, instrumentationserver 1699 confirms that the packet information corresponds to acommissioned simulation model utilizing a CRC digital signature asdescribed with reference to FIGS. 17A-17C. If the aggregate data packetcorresponds to a commissioned model, instrumentation server 1699 storesthe count data within the aggregate count event packet in a set of countdata storage files 2001 a-2001 n.

FIG. 20B depicts an exemplary aggregate count event packet 2010 inaccordance with a preferred embodiment of the present invention. Similarto aggregate data packet 1750 of FIG. 17B, aggregate count event packet2010, includes model name field 1751, CRC digital signature field 1752,and data field 1753. Within data field 1753, a cycle count field 2011contains a count value representing the number of cycles executed duringthe simulation run from which aggregate count event packet 2010 wasgenerated. A set of count value fields 2012 a-n contain the count valuesfor each of the count events instantiated within simulation model 1700in the order set forth by the count eventlist file created at modelbuild time.

Within instrumentation server 1699 depicted in FIG. 20A, the count eventdata contained in the count value fields for one or more aggregate countevent packets is stored in count data storage files 2001 a-n. Each ofcount data storage files 2001 a-n therefore contains all recorded countsfor a given predetermined time interval (typically a day) for aspecified simulation model. When instrumentation server 1699 receivesand confirms the commissioned status of aggregate count event packet2010, one of count data storage files 2001 a-n is either created orupdated as necessary to store the contents of the received aggregatecount event packet 2010.

Referring to FIG. 20C there is illustrated the contents of an exemplarycount data storage file 2001 among count data storage files 2001 a-n.Within count data storage file 2001, a cumulative cycle count field 2020represents the cumulative number of cycles simulated during everysimulation run for which count data is added into count data storagefile 2001 over the aforementioned predetermined time interval. A set ofcumulative count fields 2021 a-2021 n contain the cumulative number ofoccurrences of each corresponding count event included within countvalue fields 2012 a-2012 n for each aggregate count event packetreceived by instrumentation server 1699. In summary, when aggregatecount event packet 2010 is received and verified by instrumentationserver 1699, cycle count field 2011 is added to cumulative cycle countfield 2020 and likewise, count value fields 2012 a-2012 n are added tocorresponding cumulative count value fields 2021 a-2021 n. Count datastorage file 2001 therefore contains a cumulative total of the number ofcycles executed on the given simulation model and the number of timeseach count event has occurred over a pre-designated time interval whichin the depicted embodiment is a day.

FIG. 20D illustrates a directory structure implemented withininstrumentation server 1699 for storing count data storage files 2001a-2001 n. All count data is stored under a counter storage directory2030 on a disk storage unit 2007 within instrumentation server 1699. Asdepicted in FIG. 20D, counter storage directory 2030 is further dividedinto a two-tier subdirectory structure.

A first tier of subdirectories 2031 is utilized to associate count datacontained within a received aggregate count event packet with aparticular time period (e.g. a specific date) in accordance with apre-designated time increment. In the embodiment depicted in FIG. 20D,this pre-designated time increment is a day (i.e., one 24-hour period).Each of subdirectories 2031 therefore contains the count data for allsimulation models received on a particular date. Each of subdirectories2031 is named in a fixed manner that is derived from the date on whichthe subdirectory was created. In association with each of subdirectories2031, a second tier of subdirectories 2032 is utilized to divide thecount data received by instrumentation server 1699 on a given day intodirectories indexed on a per-model basis. Therefore, each ofsubdirectories 2032 includes all count data collected for a particularsimulation model on a particular date. As shown in FIG. 20D, each ofsubdirectories 2032 is named in a fixed manner that is derived from themodel name of the given model.

As further illustrated in FIG. 20D, each of subdirectories 2032 containsa corresponding one of count data storage files 2001 a-2001 n,containing the count data for a specified simulation model collected ona given date. The directory/subdirectory structure contained within diskstorage unit 2007 provides an efficient directory path for locatingcount event data for any given simulation model generated on aparticular date. For example, sorting count data first by day and thenby simulation model, simplifies removal of stale data frominstrumentation server 1699. Data for all active simulations models thatis obtained during an expired past time interval can be removed simplyby removing the appropriate subdirectory 2031 and its contents.

As a further improvement in processing counter queries, and inaccordance with an important feature of the present invention,instrumentation server 1699 generates and maintains a “count evententity translation table” that is indexed on a per-design-entity basissuch that all design entities contained within simulation models forwhich aggregate count event data has been received are listed, and arefurthermore associated with a list of all of the simulation models inwhich they are instantiated.

To allow for the construction of the entity translation table withininstrumentation server 1699, instrumentation load tool 464 is furtheraugmented to produce an “entitylist file” as a part of the model buildprocess for each simulation model. The entity list file lists everydesign entity within a given model in which instrumentation count eventsare instantiated. Similar to the processing of event list files, entitylist files are commissioned within instrumentation server 1699 as a partof step 1655 of FIG. 16B. Upon receiving an entity list file,instrumentation server 1699, adds the simulation model and design entityidentification contents of the entitylist file to the entity translationtable.

FIG. 20E illustrates a set of exemplary entity list files 2041 a-2041 cthat are incorporated within a count event entity translation table 2044in accordance with a preferred embodiment of the present invention. Eachof the entity list files 2041 a-2041 c includes a list of design entityidentifiers corresponding to every design entity within a respectivesimulation model (models X, Y, and Z) that include instantiatedinstrumentation count events.

Count event entity translation table 2044 maintains an index list 2046of the design entities (a, b, c, d, x, and y) having instantiatedinstrumentation count events and that are included within simulationmodels, including models X, Y, and Z, which have been commissioned oninstrumentation server 1699. A model list 2045 includes entriescorresponding to each design entity index within index list 2046denoting those simulation models that contain the design entitydesignated by the design entity index entry. The steps necessary togenerate count event entity translation table 2044 from entitylist files2041 a-2041 c are readily conceivable by one skilled in the art and aretherefore not described herein. When a simulation model isdecommissioned (i.e., records and data removed from instrumentationserver 1699), its corresponding entries within model list 2045 areremoved (potentially including removal of a design entity index when thelast model containing that design entity is removed) from count evententity translation table 2044. Instrumentation server 1699, utilizescount event entity translation table 2044 to ascertain which subset ofcount data storage files 2001 a-2001 n must be searched in response to auser count query for a given design entity or entities.

Elements within instrumentation server 1699 utilized for storing,managing, and executing user counter queries as depicted in FIG. 20F. Tocreate, delete, or modify counter queries, a user executes a counterquery interface program 2050 on general-purpose computer 1600. Counterquery interface program 2050 utilizes a graphical user interface (GUI)2051 on display 14 to allow the user to display, create, remove, or editcounter queries delivered to and stored within instrumentation server1699. A set of such counter queries 2053 a-2053 n is stored on diskstorage device 2007 within instrumentation server 1699. Each of counterqueries 2053 a-2053 n is stored in a separate file and containsinformation, described below with reference to FIG. 20H, denoting thetime at which the query is to be executed, the count data beingrequested, and the means by which to return the counter query data tothe user.

A counter query manager program 2052, residing within memory 44 ofinstrumentation server 1699, executes counter queries 2053 a-2053 n andalso returns the final output count data to the user. At regularintervals, counter query manager 2052 examines counter queries 2053a-2053 n to determine which of these queries should be run at any giveninterval. A determination to run a particular counter query is made byexamining information within the query itself, which indicates a time atwhich the query is to execute within instrumentation server 1699.

Upon determining that a specific query among counter queries 2053 a-2053n is to be executed, counter query manager 2052 spawns an instance of acounter query engine (CQE) program 2054 to process the query. Counterquery manager 2052 can spawn multiple instances of counter query engineprogram 2054 simultaneously if multiple counter queries are to beexecuted over the same interval.

Counter query engine program 2054 utilizes information within thecounter query and count event entity translation table 2044 in order tosearch appropriate count data storage files 2001 a-2001 n for thecounter information specified in counter query 2053. At the end of thissearch, counter query program 2054 produces a query report hereinaftercalled a “basic counter output report”.

Referring to FIG. 20G, there is illustrated an exemplary basic counteroutput report 2060. Within basic counter output report 2060, a firstinteger field 2061 denotes the number of simulator cycles accumulatedduring simulation runs on simulation models containing the count eventsfor which a count has been requested in the counter query. The valuestored within first integer field 2061 is derived by summing countfields 2020 (FIG. 20C) for each of the counter data storage files 2001a-2001 n that contain the count events identified in the counter query.Basic counter output report 2060 further contains a report list 2062including entries for each count event specified in the counter query.Entries within list 2062 consist of count event identifier fields 2063and integer fields 2064 which denote the number of occurrences of acorresponding count event in accordance with information obtained fromcounter data storage files 2001 a-n.

Counter queries can specify either a hierarchical or non-hierarchicalquery. In a hierarchical query, each replicated instance of any givencount event is queried independently. As such, the basic counter queryreport lists individual entries within list 2062 for each replicatedinstance of a given count event. In such a circumstance, each of countevent identifier fields 2063 corresponds to the extended eventidentifier described earlier in conjunction with FIG. 10B. Exemplarybasic counter output report 2060 represents a hierarchical report forevent “count1” within design entity “Z” within simulation model 1000described earlier with reference to FIG. 10A.

Counter queries may alternatively be non-hierarchical, wherein allreplicated instances of a given count event are grouped as a singleevent for reporting purposes. The counts for the individual instances ofthe count event are summed to form an overall count of the number oftimes the individual count event occurred in all instances.

FIG. 20G further illustrates a basic counter output report 2065 thatresults from a non-hierarchical counter query. Non-hierarchical basiccounter output report 2065 includes an event identifier 2066corresponding to the event identifier for the count events with theinstance identifier field removed. Since the various instances of agiven count event are combined into a single reported event, theinstance identifier has no meaning and is not included in thenon-hierarchical report.

Returning to FIG. 20F, after being produced by counter query engine2054, a set of basic counter output reports 2060 a-2060 n is storedwithin instrumentation server 1699 in a subdirectory 2055. Each reportwithin subdirectory 2055 is named such that the date on which it wascreated and the particular user counter query which created the reportcan be determined. Basic counter output reports are stored byinstrumentation server 1699 to enable on-going analysis of longer termtrends of the counter data as described below.

Counter query engine 2054 may optionally be utilized for post-processingof basic counter output report 2060 in order to produce a graphicalrepresentation of the counter data such as a line plot, histogram orother such representation as a final counter query report 2056. Each ofcounter queries 2053 a-2053 n specifies what, if any, additionalpost-processing to apply to basic counter report 2060 to create finalcounter query report 2056.

Final counter query report 2056 is returned to the user atgeneral-purpose 1600 over network interface 1720 by one of a number ofmeans that could include e-mail attachments, a specific file transferprotocol between instrumentation server 1699 and general-purposecomputer 1600, or copying the file to a shared file system among others.Each of counter queries 2053 a-2053 n specifies which mechanism(s) touse in returning the data to the user (it is possible to utilizemultiple methods to deliver multiple copies of final counter queryreport 2056).

With reference to FIG. 20H, there is depicted a representation of thequery fields within an exemplary counter query 2053. For illustrativepurposes, the fields within counter query 2053 are divided into fivefield groups 2068, 2069, 2081, 2084, and 2087. Field group 2068 providesgeneral information regarding the identity of the query and the identityof the user that initiated it. Within field group 2068, a field 2071contains the name of the query. Query name field 2071 provides a usefulmechanism for referring to and specifying user counter queries. Alsowithin field group 2068, a username field 2072 contains the usernameidentity of the user that initiated the query and field 2073 containsthe name of the user machine from which the query was initiated. Thisinformation is utilized to allow counter query engine 2054 to returnfinished counter report 2056 to the user.

Field group 2069 generally specifies the identity of the count events tobe queried in accordance with the remaining fields within counter query2053. Fields 2074, 2075, 2076, and 2077 correspond to the instantiation,instrumentation entity, design entity, and eventname fields in the countevent extended identifiers as described hereinbefore with reference toFIG. 10B. These fields, which can contain wildcard entries, are utilizedto specify which instrumentation count events within a simulation modelare being requested. These fields are matched by counter query engine2054 against the count event eventlist files stored on instrumentationserver 1699 to locate the desired count events within the simulationmodels searched during the query.

Field 2088 is an enable field (i.e., a flag) that determines whethercounter query 2053 is hierarchical or non-hierarchical. If the userdecides to perform a hierarchical query, instantiation identifier field2074 is used to locate count events during the query. If, however, thequery is non-hierarchical as specified by field 2088, field 2074 isignored in searching for count events. The extended event identifier fordifferent instances of a given count event differ only in theirinstantiation identifier fields (field 1030 in FIG. 10B). By ignoringthe instantiation identifier field when searching for count events,differing instances of a given count event will appear as the same countevent and be merged in reporting.

Field group 2081 generally specifies the times at which to run the queryand how far back in time the query should span. In a preferredembodiment, instrumentation server 1699 allows user counter queries tospecify days of the week a query is to run and also to specify aparticular set of hours on those days the query is run. A day field 2078is a set of seven flags specifying which days of a week a query is torun, while an hours field 2079 is a set of 24 flags indicating the hourson those days a query is to run. Field group 2081 further includes alookback field 2080 that specifies the how far back in time a query isto examine data. When a query is run, it searches stored counter datafor the day it is run and for a number of preceding days. The number ofpreceding days to search is specified by field 2080.

Field group 2084 generally specifies what, if any, report postprocessing is to be performed and the report delivery mechanism. Areport post-process field 2082 specifies the report processing, if anyto apply to produce final counter report 2056. A report delivery field2083 specifies which delivery mechanism(s) to utilize in returning thefinished counter report to the user.

Field group 2087 generally specifies an optional restriction that limitsthe search of the counter data to specific models. This feature isuseful to simulation teams that generally, in contrast to designers,prefer to query counter data with respect to a specific model or models,rather than querying count data at the design entity level. A simulationmodel search enable field 2086 holds a flag indicating whether or notcounter data searches are to be limited to specific models or not. Fieldgroup 2087 also includes a models field 2085, which may contain wildcardexpressions, specifying which models the search will be conducted withrespect to.

Referring to FIG. 201 in conjunction with FIG. 20H, there is shown aflowchart of the process by which counter query engine 2054 producesbasic counter output report 2060 from user query 2053. The processbegins at step 2090 corresponding to counter query manager 2052determining that query 2053 is ready to be run and spawning counterquery engine 2054 to process the query.

The process continues with step 2091 depicting a determination of thepossible date subdirectories 2031 (FIG. 20D) that may need to besearched. To determine these directories, counter query engine 2054examines and compares the current date with the content of lookbackfield 2080 in counter query 2053. Using this information and standarddate processing techniques well known to those skilled in the art,counter query engine 2054 produces a list of directory namescorresponding to the current date and the number of days before thecurrent date specified by lookback field 2080. These directory namescorrespond to date subdirectories 2031 that must be searched inaccordance with counter query 2053.

Proceeding to step 2092, a determination is made of the modelsubdirectories among subdirectories 2032 a-2032 n within datedirectories 2031 that are to be searched. Counter query engine 2054creates a list of possible model directories by matching design entityfield 2076 against index list 2046 of entity list translation table 2044(FIG. 20E) and creating a directory list data structure (not depicted),removing duplications, of those models that correspond to matchingdesign entities. This list represents the set of active models oninstrumentation server 1699 that contain the design entity or entitiesspecified by design entity field 2076. In this manner, counter queryengine 2054 creates a list of simulation models within the directorylist data structure that are to be searched based solely on the designentity name without user intervention. The designer need not know whichactive models on instrumentation server 1699 contain the desired designentity or entities.

If simulation model search enable field 2086 of user query 2053 isasserted (i.e., search only with respect to designated models), the listof model subdirectories is further matched against models field 2085.Those model subdirectories that do not match the content of model field2085 are removed from the list generated in response to step 2092. Inthis manner, the counter query is further restricted to the specific setof model names specified by field 2085.

Next, as illustrated at step 2093, data structures necessary to processcounter query 2053 are generated. The first data structure created is alist of directory paths relative to counter storage directory 2030 (FIG.20F) that will be searched. These directory paths are created by formingall possible combinations of the directory names obtained in steps 2091and 2092. It should be noted that some of the directory paths generatedin this manner might not actually exist within counter storage directory2030. The process that generates the directory paths generates all thepaths that need to be searched whether or not count event data hasactually been received and stored for those models or days.

It is also possible that no directory paths match the criteria specifiedby query 2053. In such a circumstance, an empty directory list datastructure is generated. The second data structure generated is a basiccount report data structure which is utilized to hold count events andcount values found during processing. The basic count report datastructure is initially empty.

The process continues at step 2094 with a determination of whether ornot the directory list data structure is empty. If the directory listdata structure is currently empty, the process continues at steps2095-2097 where the results of the counter query are reported to theuser and stored. In such a circumstance, an empty report is generatedbecause no counter data matches the criteria specified in counter query2053.

If the directory list data structure is not empty, and as illustrated atstep 2098, a determination is made of whether or not the currentdirectory in the directory list data structure exists within counterstorage directory 2030. If the directory does not exist, the processcontinues as depicted at step 2067 with the removal of the currentdirectory from the directory list data structure generated at step 2092.Processing then returns to step 2094 wherein subsequent directories inthe directory list data structure are processing accordingly.

If, as determined at step 2098, the subdirectory exists amongsubdirectories 2032 a-2032 n, query processing continues as depicted atstep 2099 with a search of the simulation model for count eventscorresponding to those specified by counter query 2053. To locate countevents corresponding to counter query 2053, counter query engine 2054matches fields 2074, 2075, 2076, and 2077 against the extended eventidentifiers in the count eventlist file for the simulation model whosedata is stored in the identified one of subdirectories 2032 a-2032 n.The names and indices of matching count events are determined to allowfor subsequent retrieval of the counter value from a corresponding oneof counter data storage files 2001 a-2001 n.

Proceeding to step 2009, matching count events are used to update thebasic count report data structure. If a matching count event discoveredin step 2099 is not present in the basic count data structure, the countevent is added to basic count data structure using the count value foundin the corresponding one of count data storage files 2001 a-2001 n.Otherwise, the count value found in count data storage file 2001 isadded to the count event entry already present in the basic count reportdata structure. This produces a cumulative total for the number of timesthe count event has occurred in the simulation models specified bycounter query 2053.

The counter query process continues at steps 2067 and 2094 wherein thedirectory is removed from the directory list data structure and adetermination is made whether or not any further subdirectories remainto be searched. If no unsearched subdirectories remain, the processcontinues at step 2095, which depicts storing the basic counter reportin directory 2055 on instrumentation server 1699 as previouslydescribed.

Next, as illustrated at step 2096, final counter report 2056 isgenerated utilizing the post-processing mechanism selected by field 2082of counter query 2053. The process is completed as depicted at step 2097with final counter report 2056 being delivered to the user by the meansselected by field 2083 of counter query 2053.

The mechanisms described above provide a means of creating, managing andexecuting counter queries that allows a designer to access counter datawithout specific knowledge of which hardware simulation models withinbatch simulation farm 1601 contain the desired design entity orentities. Furthermore, by accessing and storing count data by aparameter known to the designers, namely the design entities containingthe count event, the above described mechanisms provide a simpleinterface to access count data based solely on items known to designersas part of the creation of count events. In addition, the meansdescribed above provides for counter queries to be repeated and countdata to be returned at regular intervals without specific interventionfrom designers. In this manner, designers can provide on-goingevaluation of count data without the burden of specific intervention tomaintain the execution of queries.

In addition to obtaining design entity centric counter results, it wouldfurther be advantageous to provide a means of determining trends incounter instrumentation data within a batch simulation farm environment.In particular, it would be useful to determine differences in rates ofoccurrence of count events, scaling appropriately for the number ofsimulation cycles executed, as simulation of one or more simulationmodels, including instantiated instances of the count events, progressesover time.

To this end, and in accordance with an important feature of the presentinvention, instrumentation server 1699 implements a reporting mechanismthat compares independently collected sets of count event data for countevents specified by a given user query and produces a report showingdifferences with respect to a predetermined threshold level, among theindependently collected sets. This reporting mechanism will hereinafterbe referred to as a “count difference analyzer”.

As implemented in accordance with the embodiments depicted herein, thecount difference analyzer accepts as processing inputs either two basiccounter output reports, similar in structure to basic counter outputreport 2060, or two count data storage files among count data storagefiles 2001 a-2001 n, and returns a count difference report. By comparingbasic counter output reports, the count difference analyzer of thepresent invention determines changes in count results forinstrumentation count events present within the basic counter reports.The count difference analyzer described herein is particularly useful tocircuit designers to monitor instrumentation count event trends forcount events specified within a particular design entity or simulationmodel.

When performing relative comparisons between count data within countdata storage files 2001 a-2001 n, the count difference analyzer of thepresent invention provides an efficient means of count trend analysisfor all count events instantiated within an entire simulation model.This feature is particularly useful to simulation users whose primaryinterest is directed toward simulation results for simulation models asa whole.

Elements and processing steps required to implement a count differenceanalyzer within instrumentation server 1699 are depicted in FIGS.21A-21D. FIG. 21A depicts a system applicable within a batch simulationfarm for storing and accessing trends in count event data in accordancewith a preferred embodiment of the present invention and FIG. 21C is ahigh-level flow diagram depicting steps performed within a batchsimulation farm instrumentation server during count difference analysisperformed within the system shown in FIG. 21A. The count differenceanalysis process within the system depicted in FIG. 21A begins asillustrated at step 2102 of FIG. 21C.

In accordance with the system illustrated in FIG. 21A, counter querymanager 2052 includes program instructions (not depicted) to spawninstances of a count difference analyzer engine (CDAE) 2100. CDAE 2100is a set of program instructions resident within memory 44 ofinstrumentation server 1699 that is spawned under two generalconditions. The first of these conditions occurs, as depicted at step2104 of FIG. 21C, in accordance with the status of an additional flagfield that is added to counter query 2053 (not depicted in FIG. 20H),which if asserted, initiates a count difference analysis to be performedin conjunction with counter query 2053. When this enable flag isasserted, counter query manager 2052 spawns CDAE 2100 at the conclusionof counter query processing as described hereinabove with reference toFIGS. 20A-20I.

As depicted at step 2120, CDAE 2100 compares the current most recentlycreated basic counter output report 2060 b with the basic counter outputreport 2060 a resulting from the previously most recently executedversion the same counter query to produce a counter difference report2105 a (step 2122). As explained in further detail with reference toFIG. 21D, counter difference report 2105 a contains informationindicating any relative changes in occurrences of one or more queriedcount events in accordance with disparities between basic counter outputreports 2060 a and 2060 b. A final counter difference report 2106 isdelivered to the user by the same means as those selected within reportdelivery field 2083 of query counter 2053 (FIG. 20H) for final querycounter report 2056 (FIG. 20F).

As illustrated at step 2106 of FIG. 21C, a second condition under whichcounter query manager 2052 spawns an instance of CDAE 2100 is at theexpiration of the predetermined counter query interval (e.g., at the endof each day) for every simulation model that has received counter datafor that day. As shown at step 2108 at a pre-specified counter queryinterval, CDAE 2100 compares counter data storage files 2001 a and 2001b (possibly after conversion of the respective count data storage filesinto basic output counter report format) to produce a counter differencereport 2105 b (step 2122). In accordance with the embodiment depicted inFIG. 21A, counter data storage files 2001 a and 2001 b constitute thecounter data for a particular simulation model over sequential timeintervals (current and previous day, for example). Counter differencereport 2105 b therefore includes data relating to the relative changesin count event occurrences for all count events in the given simulationmodel between the two days.

Counter difference reports for commissioned simulation models are storedby instrumentation server 1699 and are accessible by users via agraphical user interface 2107 that communicates with counter querymanager 2052. It should be noted that counter difference reports notinitiated by a counter query (i.e., those initiated in accordance withstep 2106) do not have a specified return destination. However,instrumentation server 1699 also provides a means, not described here indetail, that allows interested users to obtain counter differencereports for a certain model or models delivered in a manner similar tothat used to deliver final counter reports. The count differenceanalysis process terminates as depicted at step 2124 of FIG. 21C.

With reference to FIG. 21B in conjunction with FIG. 21D, there areillustrated a system and a process for performing count differenceanalysis in accordance with a preferred embodiment of the presentinvention. The steps depicted in FIG. 21D provide a more detaileddescription of comparison and report generation steps 2120, 2108, and2122 of FIG. 21C.

The count difference analysis system illustrated in FIG. 21B includesCDAE 2100 receiving inputs from a pair of basic counter output reports2110 a and 2110 b for a given non-hierarchical counter query. Aresultant count difference report 2115 is generated by CDAE 2100 inaccordance with the process step described herein. As further depictedin FIG. 21B, basic counter output reports 2110 a and 2110 b includeindividual row-wise entries corresponding uniquely to a particularinstrumentation count event. Basic counter output report 2110 a providescount totals within count fields 2112 a for count events count1, count2,count3, count4, and count5, for simulation testcases performed over 9000simulation cycles as indicated within a cycle count field 2111 a.Likewise, basic count report 2110 b provides count totals within countfields 2112 b for count events count1, count2, count3, count4, andcount6, for simulation testcases performed over 18000 simulation cyclesas indicated within a cycle count field 2111 b.

The count difference analysis depicted in FIG. 21D begins at step 2126and continues with a normalization factor computation as shown at step2128. The normalization factor computation depicted at step 2128 isperformed by CDAE 2100 to normalize the count event count values withincount fields 2112 a and 2112 b to account for any difference in therespective number of simulation cycles (9000 and 18000, respectively)over which the results of basic counter output reports 2110 a and 2110 bwere obtained.

To normalize the count event values within count fields 2112 a and 2112b, CDAE 2100 computes a ratio between cycle count fields 2111 b and 2111a and then multiplies the count values within count fields 2112 a bythis ratio. In the depicted embodiment, such normalization entailsmultiplying each of the count values constituting count fields 2112 a bythe ratio (18000/9000) before further processing.

Once the count values are normalized, CDAE 2100 determines thedifferences between the normalized count values. To this end, and asillustrated at step 2130 of FIG. 21D, CDAE 2100 first determines thosecount events that have either been added or removed in their entirety toor from basic count report 2110 a and 2110 b (i.e., appear in only oneof basic counter reports 2110 a or 2110 b). Such added or removed countevents are reported in an event disparity report field 2113 of countdifference report 2115 (step 2132) and are removed from further counterdifference analysis processing.

Following the removal of data for count events that have been newlyadded or removed and for which a trend analysis is thus currentlyimpracticable, CDAE 2100 identifies which of the remaining count eventsthat have a count value of zero as reported in either of basic counteroutput reports 2110 a or 2210 b (step 2134). Such zero-value countevents correspond to counts events that were present (i.e., not removedor added) within the design entities targeted by the counter queriesfrom which both basic counter output reports 2110 a and 2110 b wereproduced, but which have either just started occurring as reported inbasic counter output report 2110 b, or have occurred as reported inbasic counter output report 2110 a but not over the reporting intervalof basic counter output report 2110 b. Such zero-value count events arereported in a zero count event field 2114 of counter difference report2115 as depicted at step 2136 of FIG. 21D. As with the added or removedcount events recorded in event disparity field 2114, the identifiedzero-value count events are immaterial to trend analysis and are notfurther considered in the present counter difference analysis.

For remaining count events, which have non-zero count values and are notnewly added or removed, CDAE 2100 computes the percentage increase ordecrease in occurrence between corresponding count events resultscontained in basic counter output reports 2110 b and 2110 a asillustrated at step 2138. As depicted at steps 2140 and 2142, thosecount events whose percentage change exceeds a predetermined thresholdvalue (for example, plus or minus 20 percent in counter differencereport 2115), are reported in a threshold exceeded field 2116 withincounter difference report 2115 and are not further considered by CDAE2100. The percentage change threshold value can be specified by the userfor each query.

The remaining count events in count reports 2110 a and 2110 b, whichhave neither been added/removed nor exceeded the percentage changethreshold value constitute those count events whose count values, afterscaling, are within the specified percentage change threshold and arenot reported in counter difference report 2115.

In the foregoing description of FIGS. 21A and 21B, the functionality ofCDAE 2100 has been described with respect to basic counter query outputreports. For cases in which CDAE 2100 must process counter data storagefiles 2001 a-2001 n (as when comparing count data for an entiresimulation model) these counter data storage files are first convertedto basic counter reports prior to commencing processing by CDAE 2100.

In accordance with a preferred embodiment of the present invention, acounter data storage file is converted to a basic counter report byplacing its cycle count field 2020 (FIG. 20C) value into cycle countinteger field 2061 (FIG. 20G) of the basic counter output report.Furthermore the count event list file for the object simulation model isutilized to generate count event identifier fields 2063 (FIG. 20G) ofthe basic count report. Finally, cumulative count values fields 2021a-2021 n (FIG. 20C) of the counter data storage file are utilized togenerate corresponding count value fields 2064 (FIG. 20G) within theresultant basic counter output report. In this manner, count datastorage files 2001 a-2001 n can be converted to basic counter reports ofthe form depicted in FIG. 20G suitable for processing by CDAE 2100.

With respect to FIGS. 21A-21D, the functionality of CDAE 2100 wasdescribed in terms of comparing count data storage files that containedcount data obtained on consecutive days or comparing basic counteroutput reports obtained from sequential executions of a given usercounter query. The extensions necessary for CDAE 2100 to compare countdata storage files obtained on non-consecutive days (separated by aweek, for example) or to compare basic counter output reports obtainedfrom non-sequential executions of a given counter query would be obviousto one skilled in the art and are included within the scope and spiritof the present invention. Furthermore, the present inventive conceptencompasses situations in which count data storage files containingcount data obtained over time intervals other than days (weeks, forexample), which could similarly be combined into two count data storagefiles and compared by CDAE 2001. Similar techniques could be applied tocombine basic count reports 2060 to produce reports spanning longerperiods of time that can be compared. In this manner, different timeperiods can be analyzed by CDAE 2100 to determine longer-term trends incounter instrumentation data. The above-described process provides ameans to analyze and report on longer term trends in counterinstrumentation data. This technique provides for automatic on-goinganalysis of counter instrumentation data for both user counter queryreports and for simulation models as a whole.

In a batch simulation farm environment, it would further be advantageousto provide a means to collect and store, with minimal redundancy, thosetestcases in which harvest events, occur. In accordance with theembodiments depicted herein, testcases in which harvest events occur arestored on a per simulation model basis, thus providing a collection oftestcases for the given simulation model that trigger the harvest eventsincorporated within the instrumentation associated with the simulationmodel. Each collection of such harvest event triggering testcases arereferred to herein as a “harvest testcase bucket” and are stored on adesignated “harvest testcase server”. Similar to a failed testcaseserver, the harvest testcase server serves as a repository for testcasescollected in response to the occurrence of a harvest instrumentationevent during execution of said testcases during simulation of saidsimulation model.

The foregoing description of count and fail event processing did notaccount for multiple testcases being executed within a given simulationrun. In practice, however, multiple testcases are executed during agiven simulation job in order to amortize the overhead of processing asimulation job over multiple testcases. When processing multipletestcases during a simulation job, the simulation model is reset at theconclusion of a testcase before the execution of the subsequenttestcase.

For count and fail events, resetting the simulation model includes amere reset of the event result registers between testcases. Therefore,between each testcase within a simulation job, counters 421 (FIG. 4B)are reset to a value of zero and fail flags 424 (FIG. 4B) are cleared.The simple resetting of count and fail event results following eachtestcase is permissible as a consequence of the fact that the practicalsignificance of count and fail events may attach to, but does not extendbeyond, the particular testcase in which they occur. This is in contrastto harvest events, whose significance lies simply in the conditionsarising during any testcase that triggers the harvest event duringsimulation of a given simulation model.

Due to this singularity of significance of harvest events, it isdesirable to minimize redundancy in the collection of testcases in whicha given harvest event occurs during multi-testcase processing of asimulation model. The present invention provides a mechanism by whichthe processing of subsequent harvest-event-triggering testcases isinfluenced by previously recorded harvest events that have occurred inprevious testcases to prevent potentially extensive redundant harvesttestcase collection in the harvest testcase bucket.

FIG. 22A illustrates elements within batch simulation farm 1601 utilizedin collecting harvest event testcases in accordance with a preferredembodiment of the present invention. FIG. 22A illustrates the contentsof memory 44 within simulation client 1701 during the execution of thetestcases within a simulation job. Prior to execution of the firsttestcase within a simulation job, RTX 1702, as part of step 1638 of FIG.16C, calls an API entry point init_harv( ) 2200. API entry point 2200 isa routine that communicates with instrumentation server 1699 and/orshared file system 1609 to obtain a list of harvest events from a“harvest hit table”, which have already occurred during simulationtesting of simulation model 1700. A separate, initially empty harvesthit table is stored for each commissioned simulation model withininstrumentation server 1699.

In response to obtaining a network copy of the harvest hit table for thecurrent model, API entry point 2200 initializes a local harvest table2201 within simulator 1735. Local harvest hit table 2201 initiallyincludes a list of harvest event names that, as evidenced by theirinclusion within the harvest hit table referenced by API entry point2200, have already occurred during simulation testing of simulationmodel 1700.

In a manner analogous to that described with respect to API entry pointdisable_events( ) 1800 in FIG. 18B, API entry point init_harv( ) 2200obtains a copy of the harvest hit table from one of two network sources.The first source is a master harvest hit table 2205 stored on diskwithin instrumentation server 1699. The second possible source is anauxiliary harvest hit table 2207 that is stored in association withgeneral-purpose computer 1607 in shared file system 1609. Master harvesthit table 2205 serves as the primary (i.e., most currently updated)version of the harvest hit table. At regular intervals, instrumentationserver 1699 copies the contents of master harvest hit table 2205 intoauxiliary harvest hit table 2207, which serves as a backup copy of theharvest hit table.

The two-file system depicted in FIG. 22A is used to maintain the harvesthit table in a flexible and robust manner within geographicallydistributed batch simulation farm 1601 for reasons similar to thosediscussed above in connection with FIG. 18A. Simulation clients thatobtain the harvest hit table from shared file system 1609 canpotentially receive a “stale” copy of the harvest hit table. Althoughthis problem may be minimized by copying master file 2205 onto auxiliaryfile 2207 on a regular interval to quickly resolve any discrepanciesbetween master file 2205 and auxiliary file 2207, harvest testcaseprocessing based on a stale harvest hit table may result in redundancywithin the harvest testcase bucket. The undesirability of suchredundancy may, however, be outweighed by the additional robustnessgained by storing the harvest hit table at different network locations.The present invention includes techniques for removing redundanttestcases stored as a result of a stale harvest hit table, along withother sources of inconsistency, as described in further detail below.

After calling API entry point 2200, RTX 1702 executes the first testcasewithin the simulation job. Once the testcase completes execution, and aspart of step 1637 of FIG. 16C, RTX 1702 calls an API entry pointrpt_harv( ) 2202, which first examines harvest flags 423 a-423 n (FIG.4B) to determine which harvest events, if any, have been triggered bythe testcase. API entry point 2202 then compares the harvest eventoccurrences as recorded by the status of harvest flags 423 a-423 n withthe content of local harvest hit table 2201 to determine if anypreliminarily non-redundant harvest events (i.e., harvest eventstriggered during the testcase that do not match those recorded in localharvest hit table 2201) have occurred. In the absence of anypreliminarily non-redundant harvest events (i.e., all harvest eventstriggered during the testcase match those recorded in local harvest hittable 2201), API entry point 2202 terminates processing and returns toRTX 1702 an indication directing RTX 1702 not to copy the currenttestcase into a harvest testcase bucket 2300. By referring to localharvest hit table 2201, API entry point 2202 prevents unnecessarycommunication with instrumentation server 1699 in those cases in whichthe harvest events that were triggered by the current testcase havealready been collected during previous simulations of simulation model1700 within batch simulation farm 1601.

If, however, one or more preliminarily non-redundant harvest events haveoccurred (i.e., at least one harvest event triggered by the testcasedoes not match any harvest event recorded in local harvest hit table2201), API entry point 2202 continues operation in one of twoalternative modes (described in further detail below) to potentiallyfurther validate the non-redundant status of the harvest event(s) inquestion. As explained in further detail below, a “direct mode” or an“indirect mode” of further non-redundant status inquiry may be pursuedby API entry point 2202. The direct non-redundant status inquiry isdesigned to ensure that only one testcase per harvest event is deliveredfrom a given simulation client for storage within harvest testcasebucket 2300. The indirect mode of non-redundant status inquiry resultsin the possibility that some redundant testcases (i.e., testcasestriggering the same harvest event) will be delivered from simulationclient 1701 to harvest testcase bucket 2300.

It should be noted that a preliminarily non-redundant harvest event, asrecorded within local harvest hit table 2201, may in fact be redundantwith respect to interim testcase activity within batch simulation farm1601. In the time interval between the initialization of local harvesthit table 2201 and the completion of the object testcase, anothersimulation client may detect and record the occurrence of one or more ofthe preliminarily non-redundant harvest events detected by simulator1735. In this case, harvest events appearing to be new in accordancewith local harvest hit table 2201, have actually already occurred duringanother testcase. In accordance with a preferred embodiment of thepresent invention, API entry point rpt_hrv( ) 2202 may undertakeadditional processing steps to ensure that an apparently newly occurringharvest event has not actually occurred elsewhere within batchsimulation farm 1601.

In the direct mode of non-redundancy status inquiry, API entry point2202 opens a direct network connection (e.g. Unix socket connection) onnetwork 1720 to a harvest manager program 2215, which executes oninstrumentation server 1699. Over this direct network connection, APIentry point rpt_hrv( ) 2202 delivers an aggregate instrumentation datapacket consisting of the contents of harvest cycle counters 422 a-422 n,harvest flags 423 a-423 n, and the name of the current testcase.

Harvest manager program 2215 first verifies that the aggregateinstrumentation data packet is associated with a simulation modelcommissioned within instrumentation server 1699 in accordance with themeans described with respect to FIGS. 17A-17C. Following packetcommissioning verification, harvest manager program 2215 then comparesthe contents of master harvest hit table 2205 with the contents of thereceived aggregate instrumentation data packet to determine if anypreliminarily non-redundant harvest event occurrences are recorded inthe master harvest hit table 2205.

Any of the apparently new harvest events that are not currently recordedin master harvest hit table 2205, are recorded by harvest managerprogram 2215 in master harvest hit table 2205 in association with thename of the current testcase. If multiple new harvest events haveoccurred in the current testcase, each of these events is recorded ashaving been triggered by the current testcase. In this manner, it ispossible to determine which specific harvest events have been triggeredby each testcase within harvest testcase bucket 2300. Subsequent todetecting and recording newly occurring harvest events in master harvesthit table 2205, harvest manager program 2215 returns an indication, overthe direct network connection on network 1720, to API entry point 2202to deliver a copy of the current testcase to harvest testcase bucket2300.

In the case that all of the apparently new harvest events (i.e., allharvest events triggered during the current testcase having nocorresponding entry in local harvest hit table 2201) are currentlyrecorded in master harvest hit table 2205, harvest manager program 2215returns an indication to API entry point 2202 over the direct networkconnection on network 1720, directing API entry point 2202 not todeliver a copy of the current testcase.

In accordance with an important feature of the depicted embodiment, onlyone simulation client may have an active network connection with harvestmanager 2215 at any given time. Prior to obtaining a communicativeconnection with harvest manager program 2215, other simulation clientsattempting to validate the redundancy status of harvest events must waituntil harvest manager program 2215 has first completed processing acurrent connection, and second has performed any necessary updates tomaster harvest hit table 2205.

In this manner, updates to master harvest hit table 2205 are serialized,thereby preventing the collection of redundant testcases for any givenharvest event. Therefore, in direct non-redundancy verification mode,once a testcase is collected within harvest testcase bucket 2300 inassociation with the recordation of a given harvest event in masterharvest hit table 2205, no further testcases will be collected inassociation with the same harvest event.

While direct non-redundancy verification mode prevents the collection ofmultiple, effectively redundant testcases for a given harvest event, itcan potentially be error prone and costly in practice. The directnetwork connection between simulation client 1701 and instrumentationserver 1699 may be costly to establish, subject to errors in highnetwork traffic conditions, and serves only one simulation client at atime. These issues are especially relevant for simulation clientsresiding in geographically distant nodes. In such circumstances, it maybe preferable to permit a certain level of redundancy in harvesttestcase bucket 2300 in order to reduce the processing and communicationoverhead required for direct non-redundancy inquiries.

To this end, an indirect non-redundancy verification inquiry is utilizedwherein API entry point 2202 uses only local harvest hit table 2201 indetermining whether or not to collect a testcase. In indirectnon-redundancy verification, API entry point rpt_hrv( ) 2202 bypassesthe step of further validating an apparently new harvest event withharvest manager 2215. Communication overhead associated with a directnetwork connection with harvest manager program 2215 is thus reduced atthe cost of potential redundancy in harvest testcase bucket 2300.

In indirect harvest mode, when, in accordance with the content of localharvest hit table 2201, an apparently new harvest event has beentriggered by a current testcase, API entry point 2202 prepares anaggregate instrumentation data packet containing the contents of harvestcycle counters 422 a-422 n, harvest flags 423 a-423 n, and the name ofthe current testcase.

This aggregate instrumentation data packet is delivered toinstrumentation server 1699 over network 1720 utilizing a non-directnetwork connection. Such a non-direct network connection may beestablished via several well-known communications mechanisms such ascustom file transfer or messaging protocols. This communicationmechanism accepts the aggregate instrumentation data packet for deliveryand subsequently returns control to API entry point 2202. The aggregateinstrumentation data packet is then delivered to harvest manager program2215 at a later time independent of the subsequent execution of APIentry point 2202 or RTX 1702. This communication mechanism allowsmultiple simulation clients to be served simultaneously in a batchfashion, and does not require a direct network connection per simulationclient to be established for harvest manager program 2215.

This is in contrast to the communication means utilized during a directnon-redundancy verification inquiry, wherein API entry point 2202 cannotreturn control to RTX 1702 until the aggregate instrumentation packethas been delivered to instrumentation server 1699, processing of thepacket has been completed, and an indication has been returned byharvest manager program 2215 of whether or not to copy the currenttestcase to harvest testcase bucket 2300. Simulation clients mustsequentially execute these collective steps to gain access toinstrumentation server 1699. This can lead to bottlenecks in performancein heavy network load situations, especially in large geographicallydistributed batch simulation farms.

When harvest manager program 2215 receives an aggregate instrumentationdata packet from a simulation client operating in indirectnon-redundancy verification mode, the commissioning status of theaggregate data packet is first validated according to the meansdescribed above in conjunction with FIGS. 17A-17C. The contents of theaggregate data packet are compared to master harvest hit table 2205 in amanner analogous to that utilized in direct harvest mode. Any harvestevents recorded in the aggregate instrumentation data packet that arenot currently included within master harvest hit table 2205 areidentified and recorded in master harvest hit table 2205. If all harvestevents present in the aggregate instrumentation data packet are alreadyrecorded in master harvest hit table 2205, the aggregate instrumentationdata packet is discarded.

As in direct non-redundancy verification mode, in indirect harvest mode,harvest manager program 2215 must processes aggregate instrumentationdata packets received from clients in a serial fashion. In this manner,accesses to master harvest hit table 2205 for simulation clientsutilizing indirect non-redundancy verification mode are also serializedand only one testcase is recorded in master harvest hit table 2205 ashaving triggered each harvest event.

However, in indirect non-redundancy verification mode, it is possiblefor multiple testcases to be delivered from simulation clients toharvest testcase bucket 2300 for the same harvest event. As one example,two simulation clients may receive the same harvest hit table contentfrom the init_harv( ) call by API entry point 2200. These simulationclients then independently execute differing testcases that both triggerthe same, preliminarily non-redundant (in accordance with theirrespective local harvest hit tables) harvest event. In both simulationclients, API entry point rpt_hrv( ) 2202 will instruct RTX 1702 toharvest their respective testcases.

Furthermore, aggregate instrumentation data packets for both testcaseswill be delivered to harvest data manager program 2215. The aggregateinstrumentation data packet received first will be recorded in masterharvest hit table 2205. The subsequently received aggregateinstrumentation data packet will be ignored due to the fact that theobject harvest event has already been recorded in master harvest hittable 2205. In this situation, the testcase reported by the simulationclient producing the second aggregate instrumentation data packet isredundantly stored within harvest testcase bucket 2300. As explained infurther detail with reference to FIGS. 23A-23C, the present inventionprovides techniques for removing these redundant testcases from theharvest testcase bucket 2300.

In a preferred embodiment, harvest manager program 2215 is configured tosimultaneously process aggregate instrumentation data packets fromsimulation clients operating in either direct or indirect non-redundancyverification mode. Therefore, simulation clients within a batchsimulation farm may operate in either direct or indirect harvest mode,according to which mode is most advantageous.

As a next step in processing in either direct or indirect mode, APIentry point 2202 updates local harvest hit table 2201 to record thoseharvest events that were triggered by the current testcase. In thismanner, subsequent testcases within the simulation job will be preventedfrom potentially erroneously harvesting another testcase for a harvestevent that has already occurred.

As an additional optional additional processing step available in eitherdirect or indirect harvest mode, API entry point 2202 may alsocommunicate with instrumentation server 1699 and/or shared file system1609 to obtain an updated copy of the harvest hit table. Typically, thisdata is obtained from shared file system 1609 to reduce thecommunication load on instrumentation server 1699.

The harvest hit table information is used to further update localharvest hit table 2201 with a more current image of those harvest eventsthat have been detected and recorded during simulation jobs executing onother simulation clients. In this manner, the processing of harvestevents by simulation client 1701 is influenced by harvest events alreadyharvested by other simulation clients since the last update of localharvest hit table 2201, and unnecessary network communication is avoidedwhile processing subsequent testcases that hit harvest events capturedby other simulation clients. This additional processing step is mostadvantageous in circumstances in which testcases take a long period oftime to complete, and therefore many additional harvest events may havebeen detected and recorded in parallel on other simulation clients.

Following data packet processing by harvest manager program 2215, APIroutine 2202 returns an indication, received either directly fromharvest manager program 2215 in direct mode or by an examination of datastructure 2201 in indirect mode, to RTX 1702 of whether or not thecurrent testcase is to be copied to harvest testcase bucket 2300. Uponreceiving an indication to save the current testcase from API entrypoint 2202, RTX 1702 delivers a copy of current testcase to harvesttestcase server 2210.

Each of a set of harvested testcases 2213 a-2213 n is stored inassociation with a particular simulation model on a disk storage device2211 associated with harvest testcase server 2210. Harvest testcaseserver 2210 also maintains a harvested testcase list 2214 that includesthe name of each testcase within harvest testcase bucket 2300. Harvestedtestcase list 2214 is updated whenever a new testcase is stored toprovide an up-to-date list of the testcase names for the harvesttestcases stored for the given simulation model.

It should be noted that certain errors can occur that prevent RTX 1702from successfully storing the current testcase on harvest testcaseserver 2210. However, when such an error occurs in direct non-redundancyverification mode, master harvest hit table 2205 has already beenupdated to indicate that the current testcase has been collected.Similarly, in indirect mode, master harvest hit table 2205 has been orwill be similarly updated (barring errors in processing the aggregateinstrumentation data packet sent to instrumentation server 1699 duringstep 2256 of FIG. 22B). Such failures to store the testcase can cause aninconsistency between master harvest hit table 2205 and harvest testcasebucket 2300 stored on instrumentation server 2210.

After the testcase has been harvested if necessary, RTX 1702 calls APIentry point clr_harv( ) 2203. API entry point 2203 clears harvest flags423 a-423 n in preparation to run a subsequent testcase. RTX 1702 thenexecutes the next testcase within the simulation job, repeating theharvest testcase verification and collection process until all testcaseswithin the simulation job have been completed.

FIG. 22B is a flow diagram depicting in greater detail the operation ofAPI entry point 2202 in accordance with a preferred embodiment of thepresent invention. API entry point 2202 begins execution at step 2250upon being called by RTX 1702. The process continues at step 2251 whichdepicts a comparison between the contents of harvest flags 423 a-423 nwith the content of local harvest hit table 2201 to determine if anyapparently new harvest events have been triggered by the currenttestcase (step 2252).

If, in accordance with the comparison shown at step 2251, no apparentlynew harvest events have occurred, the process continues as illustratedat step 2258, with the setting of an internal indication instructing RTX1702 not to harvest the current testcase. Otherwise, as depicted at step2253, an aggregate instrumentation data packet is generated containingthe contents of harvest cycle counters 422 a-422 n, harvest flags 423a-423 n, and the name of the current testcase.

Proceeding to step 2254, wherein is depicted a determination of whethernon-redundancy verification is to be performed in direct or indirectmode. If indirect mode is selected, and as illustrated at step 2256, theaggregate instrumentation packet generated in step 2253 is scheduled fora later delivery to harvest manager program 2215. If directnon-redundancy verification processing is selected by the simulationclient, API routine rpt_hrv( ) 2202 continues as shown at step 2255, bydelivering the aggregate instrumentation data packet to harvest managerprogram 2215 via a direct network connection over network 1720 tovalidate the first occurrence status of the apparently new harvestevents (step 2257).

As part of step 2257 a determination is returned to API entry point 2202from harvest manager program 2215 of whether the harvest events areactually new. If the harvest events are not new (i.e., are recorded inthe current network harvest hit table), the process continues asdepicted at step 2258, with API entry point 2202 setting an internalindication instructing RTX 1702 not to copy the current testcase toharvest testcase bucket 2300. If, as determined by the comparison ofaggregate instrumentation packet data with the network harvest hittable, the harvest events are actually new, API entry point 2202 sets aninternal indication instructing RTX 1702 to copy and deliver the currenttestcase to harvest testcase bucket 2300 (step 2259).

API entry point 2202 processing continues at step 2260, with an updateof local harvest hit table 2201 to include those harvest events thathave occurred in the current testcase. Continuing at step 2261, adetermination is made of whether or not local harvest hit table 2201should be updated to reflect those harvest events captured by othersimulation clients. If the optional update is selected, API routine 2202requests an updated image of the harvest hit table from eitherinstrumentation server 1699 and/or shared file system 1609 asillustrated at step 2263. Next, as depicted at step 2264 local harvesthit table 2201 is further updated to reflect those additional harvestevents obtained at step 2263. Processing terminates at step 2265,depicting the return of the internal indication of whether or not toharvest the current testcase set by API entry point 2202 at either steps2255 or 2256 to RTX 1702.

With reference to FIG. 22C, there is depicted a flow diagramillustrating in greater detail the operation of harvest manager program2215 in accordance with a preferred embodiment of the present invention.Harvest manager program 2215 begins processing at step 2280 andcontinues as illustrated at step 2281, with a determination of whether arequest for a direct network connection over network 1720 from asimulation client operating in direct non-redundancy verification modeis present.

If so, and as illustrated at step 2282, harvest manager program 2215receives the aggregate instrumentation data packet transmitted over thedirect network connection on network 1720 as a part of step 2255 of FIG.22B. The process continues at step 2283, with harvest manager program2215 comparing the contents of the aggregate instrumentation data packetto master harvest hit table 2205 to determine if any actually newharvest events have occurred and updating, as necessary, master harvesthit table 2205 to record these new events (step 2283).

Proceeding to step 2284, harvest manager program 2215 determines if anynew harvest events have been recorded at step 2283. If new harvestevents have been recorded at step 2283, harvest manager program 2215returns an indication through the direct network connection on network1720 instructing the simulation client to copy the current testcase intoharvest testcase bucket 2300 as depicted at step 2285. If no new harvestevents are recorded at step 2283, an indication is delivered fromharvest manager program 2215 through the direct network connection onnetwork 1720 instructing the simulation client not to collect thecurrent testcase within harvest testcase bucket 2300 as depicted at step2286.

The process continues at step 2287, which illustrates a determination ofwhether an aggregate instrumentation data packet from a simulationclient operating in indirect non-redundancy verification mode has beendelivered from simulation client 1701. If an aggregate instrumentationdata packet has been delivered, the process continues as illustrated atstep 2288, with harvest manager program 2215 receiving the aggregateinstrumentation data packet delivered to harvest manager program 2215 asa part of step 2256 of FIG. 22B. Continuing at step 2289, harvestmanager program 2215 compares the contents of the aggregateinstrumentation data packet to master harvest hit table 2205 todetermine if any actually new harvest events have occurred and updating,as necessary, master harvest hit table 2205 to record these new events.

Next, as depicted at step 2330 harvest manager program 2215 undertakesprocessing steps required to resolve inconsistencies between thetestcases collected within harvest testcase bucket 2300 and thetestcases recorded within master harvest hit table 2215. The particularprocessing steps performed during step 2330 are explained in furtherdetail with reference to FIGS. 23A-23C. In this manner, harvest managerprogram 2215 processes aggregate instrumentation data packets receivedfrom simulation clients in both direct and indirect harvest mode. Theprocess returns to step 2281 to repeat for each direct or indirectnon-redundancy inquiry delivered by the simulation client. The abovedescribed mechanism for harvesting testcases provides for a robust andefficient means to collect testcases, minimizing duplication wherepossible, that exercise harvest events in batch simulation farm 1601.

The mechanism for harvesting testcases described with reference to FIGS.22A-22C has two main sources of inconsistency between the entries withinmaster harvest hit table 2205 and the testcases recorded in harvesttestcase bucket 2300. The first of these inconsistencies, referred tohereafter as a “lost harvest testcase”, occurs when a harvest event isrecorded in master harvest hit table 2205 in association with aparticular testcase, but RTX 1702 fails to successfully store thetestcase on harvest testcase server 2210. In such a circumstance, masterharvest hit table 2205 includes a harvest event entry for a testcasethat is not actually stored within harvest testcase bucket 2300. Assuch, subsequent testcases that trigger the object harvest event willnot be collected within harvest testcase bucket 2300. Left uncorrected,this condition prevents any future collection of testcases that triggerharvest events whose recordation within master harvest hit table 2205has become effectively erroneous.

The second source of inconsistency between the entries within masterharvest hit table 2205 and the testcases recorded in harvest testcasebucket 2300, referred to hereafter as an “extraneous harvest testcase”,may occur as a result of the nature of the previously described indirectredundancy verification mode optionally undertaken by API entry pointrpt_hrv( ) 2202. As previously explained with reference to FIGS.21A-21C, an indirect non-redundancy status inquiry results in thepossibility that testcases which are redundant with respect to a givenharvest event may be collected in association with a given simulationmodel within harvest testcase bucket 2300. This is despite the fact thatonly one testcase is recorded per harvest event in master harvest hittable 2205. The collection of potentially thousands of extraneoustestcases within harvest testcase bucket 2300 is inherently undesirable.Furthermore, it is not possible to definitively determine from masterharvest hit table 2205 which harvest events are triggered by theseextraneous testcases.

It would therefore be advantageous to provide a means, hereafterreferred to as “harvest annealing”, to resolve these inconsistencies.Referring to FIG. 23A, there are depicted additional elements withininstrumentation server 1699 and harvest testcase server 2210 that areutilized in resolving inconsistencies between master harvest hit table2205 and harvest testcase bucket 2300.

As illustrated in FIG. 23A, the contents of memory 44 within harvesttestcase server 2210 and instrumentation server 1699 utilized toimplement harvest annealing include a harvest annealing program 2305 andharvest manager program 2215, respectively. At proscribed intervals,harvest testcase server 2210 initiates harvest annealing program 2305for a given simulation model. Harvest annealing program 2305 first opensa direct network connection on network 1720 to harvest manager program2215 executing on instrumentation server 1699. Harvest annealing program2305 delivers harvest testcase list 2214, which contains a list of thenames of all testcases stored on harvest testcase server 2210 for thegiven model, to harvest manager program 2215.

Harvest manager program 2215 compares the testcase name fields withinmaster harvest hit table 2205 to the entries of testcase list 2214. Anyentry in master harvest hit table 2215 whose testcase field does notcorrespond to any testcase name entry within harvest testcase list 2214indicates a lost harvest testcase that is not present in harvesttestcase bucket 2300. Detection of such lost harvest testcases resultsin the removal of the corresponding harvest event entries from masterharvest hit table 2205. Removal of these harvest event entries frommaster harvest hit table 2205 enables collection of testcases triggeringthe object harvest events during future simulation jobs.

As a further step in the comparison, harvest manager program 2215produces a list of each testcase recorded in harvest testcase list 2214which cannot be correlated with the testcase field of an entry in masterharvest hit table 2205. These testcases correspond to the extraneousharvest testcases described above. This list of extraneous testcases2302 is returned to harvest annealing program 2305 over the directnetwork connection on network 1720. Harvest annealing program 2305removes the extraneous testcases indicated in the extraneous testcaselist from harvest testcase bucket 2300.

FIG. 23B provides a more detailed illustration of the data structure andcontent of harvest testcase list 2214 and master harvest hit table 2205as they exist prior to the harvest annealing process of the presentinvention. Within harvest testcase list 2214, a name field 2360 includesdata field entries containing the names of testcases, test1, test2,test3, test4, and test5, which are stored on harvest testcase server2010 in association with simulation model 1700.

Master harvest hit table 2205 includes row-wise entries for harvestevents that have been recorded by simulation clients in the course ofsimulation on batch simulation farm 1601. Each harvest event entrywithin master harvest hit table 2205 includes an extended eventidentifier field 2362 that contains the name of the harvest event. Eachentry also includes a testcase name field 2363 that contains the name ofthe testcase that triggered the corresponding harvest event. An instanceidentifier field 2361 is also included within each entry to provide anindication of the specific instance of the harvest event that wastriggered. Harvest events may be collected in either a hierarchical ornon-hierarchical mode. In a hierarchical mode, each instance of a givenharvest event is processed independently, and a separate testcaseexercising each instance of the harvest event may be collectedaccordingly. In such a case, when determining if a reported harvestevent is new, harvest manager program 2215 considers instance identifierfield 2361.

In non-hierarchical mode, harvest events are collected in harvesttestcase bucket 2300 without regard to each specific instance of a givenharvest event. Once a testcase exercising a particular instance of aharvest event is detected and recorded, no further testcases arecollected for that event. To this end, instance identifier field 2361 isignored by harvest manager program 2215 when operating innon-hierarchical harvest mode.

Regardless of the harvest annealing mode selected by harvest managerprogram 2215, instance identifier field 2361 is present in masterharvest hit table 2205. In either mode, instance identifier field 2361maintains the potential utility in determining which specific instanceof the harvest event has been triggered by a given testcase.

In comparing the exemplary entries within harvest testcase list 2214with the entries in master harvest hit table 2205, a “lost testcase”entry 2264 contains a harvest event entry (i.e., D B5 L E4)corresponding to a testcase (i.e., test7) that is not included among thetestcase names contained in harvest testcase list 2214. Responsive tothe harvest annealing file comparison for the exemplary files depictedFIG. 23B, entry 2264 is removed from master harvest hit table 2205.

As further depicted in FIG. 23B, harvest testcase list 2214 includes twoentries, test2 and test5, which are not included within any entry withintest case name field 2363 of master harvest hit table 2205. Aspreviously explained with reference to FIG. 23A, such extraneoustestcase entries will be removed from harvest testcase bucket 2300 andharvest testcase list 2214 within harvest testcase server 2210.

Referring to FIG. 23C, there is depicted a flow diagram illustrating infurther detail the steps performed by harvest manager program 2215during step 2330 of FIG. 22C to process annealing requests from harvesttestcase server 2210. The process begins at step 2370 and proceeds tostep 2371, which depicts harvest manager program 2215 receiving harvesttestcase list 2214 from harvest testcase server 2210. Next, asillustrated at step 2372, harvest manager program 2215 sequentiallyexamines the entries within harvest testcase list 2214 and marks aninternal flag (not depicted in FIG. 23B) in master harvest hit table2205 for each entry whose testcase name field 2363 corresponds to anentry in harvest testcase list 2214.

At the conclusion of the examination of testcase list 2214, any unmarkedentries in master harvest hit table 2205 correspond to the set of losttestcase inconsistencies. In addition, while sequencing through harvesttestcase list 2214, harvest manager program 2215 creates an extraneoustestcase list containing testcase names that have no corresponding entryin master harvest hit table 2205.

The process continues as illustrated at step 2734, which depicts masterharvest program 2215 removing the unmarked entries from master harvesthit table 2205. Proceeding as depicted at step 2375, harvest managerprogram 2215 returns the extraneous testcase list to harvest testcaseserver 2210 over the direct network connection on network 1720. Harvesttestcase server 2010 will, at a later time, remove these extraneoustestcases from harvest testcase bucket 2300 and harvest testcase list2214. The harvest annealing process then terminates as depicted at step2376. The above-described process provides a means by which to resolveinconsistencies arising in the process of harvesting testcases in ageographically distributed batch simulation farm 1601.

In the means described above for creating and managing instrumentationevents in a batch simulation farm, instrumentation events are generatedfrom HDL code or, as described above in conjunction with FIGS. 12A and12B, from unconventional comments. The HDL code and/or unconventionalcomments are compiled into instrumentation entities within thesimulation model by instrumentation load tool 464. In the followingdescriptions of FIGS. 24A-24C, a logic function that detects theoccurrence of an instrumentation event (represented by logic 402 in FIG.4A as one example) will be referred to as “instrumentation logic”, andinstrumentation events created from HDL code or unconventional commentswill be referred to as “HDL instrumentation events”.

As described above, instrumentation server 1699 controls HDLinstrumentation events utilizing a number of API entry points called byRTX 1702 during execution of testcases. Furthermore, instrumentationserver 1699 retrieves data from HDL instrumentation events for storageand processing utilizing additional API entry points called by RTX 1702.These mechanisms provide an efficient means by which an HDL circuitdesigner can generate HDL instrumentation events that are managed andrecorded in a largely automatic fashion by instrumentation server 1699.Under some circumstances, however, utilizing HDL code and/orunconventional comments to produce instrumentation logic can prove to beinefficient.

In particular, it is often the case that complex instrumentation eventsoccur over many cycles and are composed of temporally complexinteractions of a large number of signals within the given simulationmodel. Detecting such instrumentation events typically requires acomplex functionality that necessitates a correspondingly complexmodeling data structure. In such a circumstance, using HDL code orunconventional comments to create the necessary instrumentation logiccan be difficult and inefficient.

In general, the instrumentation functionality required to detect suchcomplex instrumentation events is usually more effectively createdutilizing a high-level programming language such as C or C++. High-levelprogramming languages typically provide more direct support for complexdata structures and provide features such as dynamic memory allocation,among others, that allow for the more efficient generation offunctionality suited to the detection of complex instrumentation events.Furthermore, simulation engineers are often not primarily conversant inHDLs, but instead rely on high-level programming languages such as C orC++.

It would therefore be advantageous to provide a means by which togenerate and process instrumentation events, hereafter referred to asRTX instrumentation events, under RTX control utilizing a high-levellanguage such as C or C++. Such a means would enable simulationengineers to more efficiently generate complex instrumentation eventswith the additional flexibility inherent in high-level programminglanguages.

As explained in further detail hereinbelow, RTX instrumentation events,unlike HDL instrumentation events, can be used to track the occurrenceof events that are not composed of sequences of signals within a givensimulation model. As an example, conventional RTXs typically receive asinputs a number of control parameters that guide the subsequent behaviorof the RTX during execution of a given simulation model. It is oftenuseful to be able to determine the number of testcases that have beenexecuted with a particular control parameter set to a particular one ofits possible legal values. In this way, it is possible to determine howoften the simulation model has been exercised in each of the possiblemodes controlled by the given control parameter. Likewise it is possibleto extend this notion in order to collect statistics on combinations ofcontrol parameters and their respective values for different simulationruns.

Such RTX control parameters are outside the scope of the simulationmodel and therefore are not directly accessible by HDL instrumentationevents that must ultimately take as inputs signals within the simulationmodel itself. In accordance with the principles set forth herein, anRTX, such as RTX 1702, may generate a number of RTX countinstrumentation events to collect statistics related to the RTX inputparameter values for each executed testcase. Specifically, at thebeginning of each testcase, the RTX triggers count events correspondingto the values assigned to the input RTX control parameter values. At theconclusion of the testcase, the resultant counts for the triggered countevents are communicated to the instrumentation server for storage andanalysis.

The present invention specifies additional API entry points that allowan RTX, utilizing a high-level programming language such as C or C++, togenerate and process RTX instrumentation events. In what follows, thehigh-level language programming code formulated to detect the occurrenceof an RTX instrumentation event will be referred to as “instrumentationcode” to distinguish it from instrumentation logic for HDLinstrumentation events that is specified by HDL code or unconventionalcomments. Instrumentation code for RTX instrumentation events that arerelated to signals within a given simulation model typically consists ofa number of calls to API entry point GETFAC( ) to obtain signal valueswithin the model, and also includes program instructions that operate onthe obtained signal values in order to detect the object instrumentationevent.

In accordance with the system of the present invention, RTXinstrumentation events and HDL instrumentation events are respectivelymaintained as distinct groups with separate namespaces. This namespaceseparation prevents name collisions between HDL and RTX instrumentationevents, and provides a barrier between the activities and goals of HDLchip designers creating HDL instrumentation events and simulationengineers creating RTX instrumentation events.

To support RTX instrumentation events, API entry points that interactwith individual instrumentation events, such as API entry point 1802,set_fail_mask( ), are augmented with an additional parameter thatspecifies whether the routine will process an RTX instrumentation eventor an HDL instrumentation event. API entry points, such as rpt_fails( ),rpt_counts( ), rpt_harv( ), disable_events( ), etc., which facilitatecontrol of an entire class of HDL instrumentation events, or whichrecord data from an entire class of HDL instrumentation eventssimultaneously, are similarly enhanced to concurrently support RTXinstrumentation events. Specifically, these API entry points areaugmented to generate and deliver aggregate instrumentation data packetsfor the RTX instrumentation events, and to receive from instrumentationserver 1699, separate control information utilized to control the RTXinstrumentation events. For example, API entry point 1900, rpt_fails( ),is enhanced to enable delivery of two aggregate instrumentation datapackets to instrumentation server 1699: one containing fail eventinformation for any HDL instrumentation events present in the simulationmodel; and one containing fail information for any generated RTXinstrumentation events.

FIG. 24A depicts data structure representations of API entry pointsdesigned to support the generation and operation of RTX instrumentationevents in accordance with a preferred embodiment of the presentinvention. Specifically, an API entry point 2400, create_event( ), whencalled by RTX 1702, initiates generation and naming of RTXinstrumentation events. FIG. 24A further illustrates an API entry point2401, trigger_event( ), which is called by RTX 1702 to indicate theoccurrence of the RTX instrumentation event generated in response to APIentry point 2400. API entry point 2400 returns a pointer 2402 to thegenerated RTX instrumentation event. Pointer 2402 is subsequently usedas an input parameter to API entry point 2401. When API entry point 2401is called with a particular pointer parameter 2403, the correspondingRTX instrumentation event generated in reference to that pointer istriggered as described in further detail with reference to FIG. 24D.

Within API entry point 2400, a type parameter 2409 specifies the RTXinstrumentation event type (e.g. fail, count, harvest, etc.). Additionalnaming specification parameters 2410, 2411, 2412, and 2413, specify thename of the RTX instrumentation event in accordance with HDLinstrumentation event naming convention. Each RTX instrumentation eventis therefore typically identified by an extended event identifierconsisting of the four fields explained with reference to FIG. 10B.

As further explained with reference to FIG. 10, extended eventidentifiers for HDL instrumentation events are derived at model buildtime directly in accordance with the structure of the simulation modeland the names assigned by unconventional comments to the HDLinstrumentation events. For RTX instrumentation events, in contrast. RTX1702 directly sets the extended event identifier fields at runtimeutilizing API entry point 2400, and furthermore ensures that each RTXinstrumentation event within a given class (fail, count, harvest, etc.)is generated with a unique name. Fields 1030, 1032, 1034, and 1036(described with reference to FIG. 10B) of the extended event identifierfor an HDL instrumentation event are specified for an RTXinstrumentation event by naming specification parameters 2410, 2411,2412, and 2413, respectively.

The extended event identifiers for RTX instrumentation events servefunctions analogous to those served by extended event identifiers forHDL instrumentation events. For example, extended event identifiers forRTX instrumentation events are used to form eventlist files for the RTXinstrumentation events. These eventlist files are created during eachsimulation model test run and are commissioned on instrumentation server1699 in the same manner as the eventlist files associated with HDLinstrumentation events. Furthermore, RTX instrumentation events whichshare the same values for type parameter 2409, instrument code parameter2411, design entity parameter 2412, and instrumentation event parameter2413, while having differing values for instance parameter 2410, areprocessed by instrumentation server 1699 as differing instances of thesame instrumentation event (analogous to the treatment byinstrumentation server 1699 of instrumentation events that arereplicated instances of a given HDL instrumentation event).

When commissioning eventlist files, instrumentation server 1699generally does not distinguish between eventlist files for HDLinstrumentation events generated during model build, and eventlist filesfor RTX instrumentation events generated by RTX 1702. Instrumentationserver 1699 can therefore support and process RTX instrumentation eventsutilizing aforementioned instrumentation event processing mechanisms,(i.e., as if the RTX instrumentation events were HDL instrumentationevents associated with another simulation model).

To enable instrumentation server 1699 to reuse the same processing meansemployed for HDL instrumentation events for processing RTXinstrumentation events, it is necessary to provide a means to assign theequivalent of a simulation model name. As utilized herein, thisequivalent to the simulation model name is referred to as the “rtxname”, which is specified for a group of RTX instrumentation events. Asfurther illustrated in FIG. 24A, an API entry point 2405, set_rtxname(), receives an input string parameter 2406, which specifies the rtx nameto be associated with the RTX instrumentation events generated inaccordance with API entry point 2400. The rtx name associated with agroup of RTX instrumentation events serves an analogous role as thesimulation model name for HDL instrumentation events. For example, themodel name field 1751 (FIG. 17B) for an aggregate instrumentation datapacket corresponding to a set of RTX instrumentation events, containsthe rtx name specified by API entry point 2405 for the corresponding RTXinstrumentation events.

The set of HDL instrumentation events associated with a given simulationmodel is fixed at model build time, allowing instrumentation server 1699to validate aggregate instrumentation data packets according to themeans described above in conjunction with FIGS. 17A-17C. Likewise, theset of RTX instrumentation events associated with a given RTX name isalso fixed. To this end, RTX 1702 must ensure that for a given rtx name,the same set of RTX instrumentation events are generated by eachsimulation run performed by RTX 1702.

If RTX instrumentation events must be added or removed from RTX 1702, anew version of RTX 1702 is produced and tested in a series of foregroundsimulation runs. Once debugged, a new RTX name is assigned to the newversion of RTX 1702, which is run once to create eventlist filescorresponding to the new rtx name and the new set of RTX instrumentationevents. These new eventlist files are commissioned on instrumentationserver 1699 to inform instrumentation server 1699 of the new RTXversion. Instrumentation server 1699 may then process data associatedwith the RTX instrumentation events present in the new version of RTX.In essence, each generation of a different set of RTX instrumentationevents that constitute a different version of the RTX, is analogous tonew versions of a simulation model being produced to alter the set ofHDL instrumentation events within that model.

An additional API entry point 2407, write_eventlists( ), is called byRTX 1702 to produce the eventlist files for the RTX instrumentationevent generated by API entry point 2400. Typically, API entry point 2407is not called on every execution of RTX 1702, but rather is called onlyin response to the generation of a new version of RTX 1702. Thegenerated eventlist files are then commissioned on instrumentationserver 1699.

Referring now to FIG. 24B, there is depicted the contents of memory 44utilized during processing of a simulation job that employs RTXinstrumentation events performed with respect to simulation model 1700within simulation client 1701. For clarity of explanation of thedepicted embodiment, it is assumed that the simulation job includes onlya single testcase. The extensions necessary to support multipletestcases within the simulation job are readily apparent to one skilledin the art.

In the depicted embodiment, RTX 1702 begins the process of generatingRTX instrumentation events by calling API entry point 2405 to assign anrtx name to the set of RTX instrumentation events that will begenerated. API entry point 2405 produces an rtx name data structure2441, which is utilized by API entry point 2407 to name the createdeventlists. Rtx name data structure 2441 is also used by those API entrypoints, such as rtp_harv( ) for example, that create aggregateinstrumentation data packets, to specify the value of model name field1751 within the aggregate instrumentation data packets.

RTX 1702 subsequently makes a series of calls to API entry point 2400,create_event( ), to generate the desired RTX instrumentation events. Foreach RTX instrumentation event, API entry point 2400 creates an eventdata structure 2440. Event data structure 2440 consists of two primaryelements: a disable flag 2445 that serves to disable the RTXinstrumentation event in a manner similar to latch 507 of FIG. 5A, anddata element 2446 that stores the contents of the RTX instrumentationevent (i.e., a count field for a count event, a flag and a cycle countfield for a harvest event, and a flag for a fail event). As a furtherstep in generating RTX instrumentation events, API entry point 2400creates an RTX instrumentation eventlist data structure 2442, whichlists the extended event identifiers and event types for each of theobject RTX instrumentation events. RTX instrumentation eventlist datastructure 2442 is utilized by API entry point 2407, write_eventlists( ),to create the RTX instrumentation eventlist files. If necessary, RTX1702 then calls API entry point 2407, write_eventlists( ), to create theeventlist files for the created RTX instrumentation events. Aspreviously explained, API entry point 2407 is typically only called fora single, initial execution of RTX 1702 to create the RTXinstrumentation event eventlist files. Once the RTX instrumentationevent eventlist files are created and subsequently commissioned withinstrumentation server 1699, API entry point 2407 is no longer calledfor the newly defined version of RTX 1702.

RTX instrumentation event processing continues with RTX 1702 callingthose event initialization API entry points, such as init_harv( ),disable_events( ), etc., utilized to initialize processing for HDL andRTX instrumentation events. As explained with reference to the foregoingfigures, these API entry point routines are augmented to operate onevent data structures, such as event data structure 2440 a-2440 n, whenprocessing RTX instrumentation events. These routines further utilizertx model name data structure 2441 in order to determine, by rtx name,the proper set of control files stored on instrumentation server 1699associated with the given RTX instrumentation events.

After calling the appropriate event initialization API entry points, RTX1702 typically enters a main processing loop 2450, which serves toprogress simulation model 1700 through simulation cycles duringexecution of the testcase. Main processing loop 2450 begins withinitialization processing steps required for the execution of asimulation cycle. After completing this initialization processing, mainprocessing loop 2450 proceeds with execution of a number ofinstrumentation code blocks 2451 a-2451 n, wherein each ofinstrumentation code blocks 2451 a-2451 n corresponds to a single RTXinstrumentation event. Each of instrumentation code blocks 2451 a-2451 ntypically consists of a number of calls to an API entry point GETFAC,which retrieves the value of signals within simulation model 1700.Program instructions within an instrumentation code block 2451 thenprocess these signal values to determine if the corresponding RTXinstrumentation event has occurred. If so, API entry point 2401,trigger_event( ), is called to record the occurrence of the RTXinstrumentation event. Within main loop 2450, RTX 1702 then calls an APIentry point cycle( ) (not shown in FIG. 24B), which advances simulationmodel 1700 a single simulation cycle. Main loop 2450 continues in thisfashion until the testcase has completed.

Upon completed execution of the testcase, RTX 1702 calls those eventpost-processing API entry points, such as rpt_harv( ), rpt_count( ),etc., which then report information collected for HDL and RTXinstrumentation events. As previously explained, these post-processingroutines are augmented to operate on event data structures, such asevent data structures 2440 a-2440 n, as part of processing RTXinstrumentation events. Furthermore, these post-processing routinesforward rtx model name data structure 2441 to set model name field 1751in aggregate instrumentation data packets delivered to instrumentationserver 1699 for the RTX instrumentation events.

With reference to FIG. 24C, there is shown a flow diagram depictingsteps performed by RTX 1702 during the generation and processing of RTXinstrumentation events in accordance with a preferred embodiment of thepresent invention. The process beings at step 2460 and proceeds to step2461, which depicts RTX 1702 setting the rtx name by calling API entrypoint 2405. The process continues as illustrated at step 2462, with thegeneration of the RTX instrumentation events by a series of calls to APIentry point 2400, create_event( ).

Next, a determination is made of whether or not to write the RTXinstrumentation event eventlist files, as depicted at step 2463. If theeventlist files are to be written, processing continues as illustratedat step 2464, with the eventlist files being written in response to acall to API entry point 2407, write_eventlists( ). Otherwise processingcontinues at step 2464, which depicts the execution of instrumentationcode blocks 2451 a-2451 n. Proceeding to step 2465, a determination ismade of whether the testcase has completed. If the testcase has notcompleted, the process returns to step 2464 for continued execution ofinstrumentation code blocks 2451 a-2451 n for the subsequent simulationcycle. Otherwise processing terminates as illustrated at step 2466.

In accordance with the RTX instrumentation event processing disclosedherein, API entry point 2401, trigger_event( ), must undertake certainprocessing steps in order to allow for the selective disabling of RTXinstrumentation events. FIG. 24D illustrates a flow diagram of stepsdepicting in greater detail the operation of API entry point 2401.

As depicted in FIG. 24D, API entry point 2401 begins processing at step2470 upon being called by RTX 1702. Next, as illustrated at step 2471, adetermination is made of whether or not disable flag 2445 for the givenRTX instrumentation event is set. If disable flag 2445 is set, theprocess terminates as depicted at step 2473. Otherwise, the processingcontinues as illustrated at step 2472, with API entry point 2401updating data element 2446. The exact nature of the update to dataelement 2446 varies with the RTX instrumentation event type. Forexample, for an RTX counter instrumentation event, the counter withindata element 2446 is incremented. Following the update to data element2446, the process terminates at step 2473.

It should be noted that disable flag 2445 serves to prevent API entrypoint 2401 from operating the corresponding RTX instrumentation event.In accordance with the present invention, selective disablement of RTXinstrumentation events is enabled by augmenting API entry point 1800 tocall an enhanced version of API entry point 1802, set_fail_mask( ). Thisenhanced API entry point 1802 is constructed to set disable flag 2445when processing RTX instrumentation events. In this manner, RTX 1702 caninitiate selective disablement of RTX instrumentation events.

The above-described mechanisms enable the creation and operation ofinstrumentation events under RTX control in a high-level programminglanguage such as C or C++. In this manner, simulation engineers inparticular are able to create instrumentation events utilizing a morerobust programming language with greater efficiency for complexinstrumentation events. Furthermore, RTX instrumentation events, asdescribed herein, provide the capability of detecting occurrences ofevents which reside outside the scope of a simulation model.

The means described above allows for the creation of RTX instrumentationevents by simulation engineers utilizing high level programminglanguages such as C or C++. It would be further advantageous to providea means for detecting RTX instrumentation events associated with an HDLdesign entity that is replicated as multiple instantiations within asimulation model. As described above, the HDL-based instrumentationlogic utilized to detect an HDL instrumentation event is automaticallyreplicated for each instance of an HDL design entity. As explained withreference to FIGS. 4A-4E, this is accomplished at model build time bycreating a separate instance of the instrumentation code for eachinstance of the design entity. In this manner, the instrumentation logicfor an HDL instrumentation event is generated without concern or regardfor the number or location of the instances of the given design entity.

However, in the means described above, no means for replication ofinstrumentation code for RTX instrumentation events based on the numberand location of the replicated instances of the monitored design entityis provided. The means described above enables creation of specified RTXinstrumentation events, but is unaware of the internal structure of thesimulation model on which it is operating.

Without such simulation model structure awareness on the part of theRTX, an RTX is required to be cognizant of the internal structure,including the number and location of replicated instances, of designentities for which RTX instrumentation events are desired for all thedifferent simulation models containing these entities. Such arequirement places a considerable bookkeeping burden on the simulationengineer. The present invention alleviates this problem by associatingone or more “instrumentation code modules” with HDL design entities in asimulation model. As explained in further detail below, theinstrumentation code modules are dynamically loaded and processed by theRTX to provide a centralized mechanism for automatically instrumentingeach instance of the given HDL design entity within any given simulationmodel in which the design entity is incorporated.

Discovery of the instances of relevant design entities for whichautomatically replicated RTX instrumentation events are desired isenabled by determining the internal structure of each simulation model.To this end, model build tool 446 (depicted in FIG. 4D) includes furtherprocessing and program instruction means for inserting a “designentitylist data structure” into each simulation model 480 at model buildtime. The design entitylist data structure contains informationidentifying all design entities instantiated within the simulationmodel. The identification of all constituent design entities, includingthose containing no instantiated HDL instrumentation events, on aper-simulation model basis enable a subsequent identification of designentity instances for which it is desired to associate an automaticallyreplicated RTX instrumentation event. Since the method of the presentinvention does not permit the association of RTX instrumentation eventswith HDL instrumentation entities, instrumentation entities within asimulation model are excluded from the design entitylist data structure.

FIG. 25A depicts a high-level block diagram representation of simulationmodel 329 of FIG. 4B augmented with a design entitylist data structure2500. Design entitylist data structure 2500 includes entries 2503 a-2503i each representing one instance of a design entity within simulationmodel 329. Each of entries 2503 a-2503 i further includes aninstantiation identifier field 2501 and a design entity name field 2502.The data structure of entries 2503 a-2503 i thus identifies eachinstance of any given design entity within the given simulation model.

Automatic replication of RTX instrumentation events for a given repeateddesign entity within a simulation model is enabled by the generation andprocessing of dynamically loaded “instrumentation code modules”. Thesemodules consist of high level program language instruction code withdistinct entry points, fixed by convention, that generate and processRTX instrumentation events associated with the instances of a givendesign entity within a simulation model. API routines described above inconjunction with FIGS. 24A-24D are utilized by the instrumentation codemodules to generate and process RTX instrumentation events.

In accordance with a preferred embodiment of the present invention,instrumentation code modules are dynamically loaded and bound to an RTXat runtime using a technique well known to those skilled in the artreferred to as “dynamic loading”. Essentially all modern operatingsystems, including but not limited to Microsoft Windows®, UNIX®, andLinux®, provide dynamic loading. In dynamic loading, a module containingprogram code and certain defined entry points is loaded into memory 44of a general purpose computer and linked to a currently running program,such as an RTX. The program that is dynamically loading a module canthen call entry points within the dynamically loaded module and therebyexecute programming instructions contained within the dynamically loadedmodule. Likewise, subject to certain constraints in the way thedynamically loaded module is bound, the dynamically loaded module cancall certain entry points, such as GETFAC( ), CREATE_EVENT( ),TRIGGER_EVENT( ), etc. within the currently running program and itsenvironment.

Utilizing the teachings of the present invention, one or moreinstrumentation code modules are created for each HDL design entity thatis to be instrumented using RTX instrumentation events. Each of thesemodules contains fixed entrypoints described below and serves to createand monitor a set of RTX instrumentation events associated with theinstances of the given HDL design entity. However, conventional dynamicloading permits only one copy of a given instrumentation code module tobe bound to a running program (i.e., RTX). Therefore, it is not possibleto bind a separate instance of the instrumentation code module to eachinstance of a given HDL design entity to achieve an analogous result aswith automatically replicated HDL instrumentation modules. Consequently,an instrumentation code module utilizes a single set of programinstructions and dynamically created data structures to monitor thedifferent instances of the given design entity within the simulationmodel.

With reference now to FIG. 25B, there is shown a representation ofinstrumentation code module 2510 having program entrypoints 2511, 2512,and 2514. Entrypoint 2511, create_instance( ), is called to promptprogram code within instrumentation code module 2510 to create the datastructures necessary for a given instance of a design entity. A separatecall to entrypoint 2511 is made for each instance of the given designentity within the simulation model. An instance name input parameter2513 is a string parameter consisting of the hierarchical instanceidentifier for the given instance of the design entity.

After all of the instances of the given design entity have beenregistered with instrumentation code module 2510 by means of calls toentry point 2511, entrypoint 2512, evaluate( ), may be called to executeprogram instruction steps within instrumentation code module 2510 whichmonitor the simulation model to detect occurrences of the desired RTXinstrumentation events. These program instruction steps utilize the APIentry points described above in conjunction with FIG. 24A to trigger RTXinstrumentation events.

Instrumentation code modules having entrypoints 2511, 2512, and 2514 aredesigned to facilitate monitoring of the RTX instrumentation eventswithin a given design entity. Additional API entrypoint routines,load_modules( ) and execute_modules( ), provided by the presentinvention and described later, are utilized to manage dynamicallyloading instrumentation code modules and calling entrypoints 2511, 2512,and 2514. Entrypoint load_modules( ) utilizes API entry point 2514,entity_name( ), to determine the name of the design entity thatinstrumentation code module 2510 is to be associated with. Entrypoint2514 returns a pointer (not shown in FIG. 25B) to a string containingthe name of the given design entity.

Referring to FIG. 25C, there is depicted data structure representationsof API entry points designed to manage dynamically loading and callinginstrumentation code modules. An API entry point 2520, load_modules( ),is utilized to load and initialize those dynamic instrumentation codemodules required by the current simulation model. An input parameter2522 is utilized to indicate the location on a disk storage system wherethe instrumentation code modules are stored. An entrypoint 2521,execute_modules( ), is utilized to call entrypoint 2512 of theinstrumentation code modules during each cycle of simulation to allowthe instrumentation code modules to monitor their respective designentities for the occurrence of RTX instrumentation events.

With reference to FIG. 25D, there is depicted the contents of memory 44utilized during processing of a simulation job that employsinstrumentation code modules processed with respect to simulation model1700 within simulation client 1701. For clarity of explanation of thedepicted embodiment, it is assumed that the simulation job includes onlya single testcase. The extensions necessary to support multipletestcases within the simulation job are readily apparent to one skilledin the art. In the depicted embodiment, RTX 1702 begins the process ofprocessing instrumentation code modules by first calling API entry point2520 to load and initialize instrumentation code modules 2510 a-2510 n.API entry point 2520, under control of input parameter 2522, searchesdisk storage unit 2007 to locate instrumentation code modules 2510a-2510 n.

API entrypoint 2520 processes each instrumentation code module 2510according to a series of steps. First, entrypoint 2520 dynamically loadsan instrumentation code module 2510 into memory 44 of simulation client1701. API entry point 2520 then calls entrypoint 2514, entity_name( ),of each instrumentation code module 2510 to determine, by design entityname, which HDL design entity each instrumentation code module 2510 isto be associated with. API entry point 2520 then searches design entitydata structure 2500 to determine if the HDL design entity identified inthe preceding step is instantiated within simulation model 1700. If thedesired design entity is not instantiated within simulation model 1700,API entry point 2520 removes the object instrumentation code module frommemory 44 of simulation client 1701 and continues processing with thenext instrumentation code module on disk storage unit 2007.

If, however, one or more instances of the desired design entity arepresent within simulation model 1700, API entrypoint routine 2520responds by calling entrypoint 2511 in instrumentation module 2510.Entrypoint 2511 is called by API entrypoint 2520 once for eachinstantiation of the desired design entity, and is passed thehierarchical instance identifier for the instance of the design entity.In this manner, instrumentation code module 2510 is made aware of thelocation of every instance of the desired design entity. Instrumentationcode module 2510 responds to the instance calls to 2511 by generatinginstance data structures 2530 a-2530 n. The instance data structures2530 a-2530 n contain the information, among other things thehierarchical instance identifier, necessary to monitor RTXinstrumentation events in each instantiation of the object designentity. Furthermore, upon successful binding and initialization ofinstrumentation code module 2510, API entry point 2520 adds an entry toan instrumentation code module table 2550. Instrumentation code moduletable 2550 consists of a list of pointers to the successfully boundinstrumentation code modules 2510. This process is repeated for allinstrumentation code modules found on disk storage unit 2007.

After processing each of instrumentation code modules 2510 a-2510 n, RTX1702 enters a main processing loop 2450, which processes simulationmodel 1700 through simulation clock cycles during execution of thetestcase. At the conclusion of each sequence through main processingloop 2450, RTX 1702 calls API entry point 2521 to executeinstrumentation code modules 2510 a-2510 n. Entrypoint 2521 utilizesinstrumentation code module table 2550 to call entry point 2512,evaluate( ), within each instrumentation code module 2510. The programinstructions within instrumentation code modules 2510 a-2510 n utilizecorresponding instance data structures 2530 a-2530 n to determine thelocations of the design entity instances and any state informationassociated with the desired RTX instrumentation events. Further, theseprogram instructions utilize the means described above in conjunctionwith FIGS. 24A-24D to monitor and record the occurrence of RTXinstrumentation events.

FIG. 25E is a flow diagram depicting steps performed by API entry point2520 during loading and initialization of instrumentation code modules2510 a-2510 n. The process begins at step 2888 and proceeds to step2551, which depicts API entrypoint 2520 determining whether or not anyinstrumentation code modules on disk storage unit 2007 remainunprocessed for a simulation job. If all instrumentation code moduleshave been processed or none are present, the process terminates asillustrated at step 2552. API entrypoint 2520 can therefore be calledeven if no instrumentation code modules are present on disk storage unit2007. If, however, an instrumentation code module 2510 remains to beprocessed, the process continues at step 2553 which depicts APIentrypoint 2520 loading and the given instrumentation module into memory44 of simulation client 1701. Continuing with step 2554, API entrypoint2520 calls entrypoint 2514 to determine the name of the design entitythe loaded instrumentation code modules is to be associated with.

Next, as illustrated at step 2555, API entrypoint 2520 searchesinstrumentation code module table 2550 to determine if any instances ofthe design entity identified at step 2554 exist in the currentsimulation model. If no such instances exist, the process continues atstep 2556, which depicts API entrypoint 2520 removing theinstrumentation module from memory 44. Otherwise, the process moves tostep 2557, which depicts API entrypoint 2520 calling entrypoint 2511 ininstrumentation code module 2510 for each instance of the desired designentity listed in instrumentation code table 2550. In this manner,instrumentation code module 2510 is made aware of the instances of thedesign entity present in the simulation model and can create instancedata structures 2530 a-2530 n. The process then moves back to step 2551to repeat the process for each of the instrumentation code modules 2510a-2510 n present on disk storage unit 2007.

With reference to FIG. 25F, there is shown a flow diagram depictingsteps performed by API entry point 2521 in executing instrumentationcode modules 2510 a-2510 n during each sequence through main processingloop 2450. The process begins at step 2570 and proceeds to step 2571,which depicts API entrypoint 2520 determining whether or not anyinstrumentation modules have been successfully loaded by examininginstrumentation code module table 2550. If all instrumentation codemodules have been processed or none are present, the process terminatesas illustrated at step 2572. API entrypoint 2520 is therefore calledeven if no instrumentation code modules were successfully bound by APIentrypoint 2520. If, however, there is at least one instrumentation codemodule remaining to be processed, the process continues at step 2573which depicts entrypoint 2521 utilizing the corresponding pointer ininstrumentation code module table 2550 to call entrypoint 2512,evaluate( ), in instrumentation code module 2510. As illustrated at step2571, the process is repeated for each of the instrumentation codemodules 2510 a-2510 n present in memory 44 of simulation client 1701.

The embodiments described with reference to FIGS. 25A-25F provide amechanism for the efficient creation and management of instrumentationcode modules that monitor RTX instrumentation for all instances of agiven design entity within a simulation model. An RTX programmer needonly insert calls to API entrypoints 2520 and 2521 to enable thismechanism, resulting in a significant reduction in effort for theoverall RTX coder. In addition, the simulation engineer creating theinstrumentation code module need no longer be aware of the variousnumber and locations of the design entity for the various simulationmodels. The present invention automatically determines the modelconfiguration and passes this information to the instrumentation codemodule, resulting in increased efficiency and portability of theinstrumentation code modules.

Referring again briefly to FIGS. 20A-20C, it has heretofore been assumedthat following each simulation run, an aggregate count event packet 2010containing count data pertaining to the simulation run is reported backto instrumentation server 1699. When aggregate count event packet 2010is received and verified by instrumentation server 1699, cycle countfield 2011 is added to cumulative cycle count field 2020 and likewise,count value fields 2012 a-2012 n are added to corresponding cumulativecount value fields 2021 a-2021 n within a count data storage file 2001.The count data storage file 2001 as hereinbefore described thereforecontains a cumulative total of the number of cycles executed on thegiven simulation model and the number of times each count event hasoccurred over a pre-designated time interval, for example, a calendarday.

The present invention recognizes that it would often be advantageous fora simulation engineer or designer to know not only cumulative countevent totals, but also the maximum and/or minimum count event values fora count event over the pre-determined time interval. In accordance withone embodiment of the present invention, this additional functionalityis supported by identifying, in the instrumentation descriptor commentsutilized to instrument an HDL design entity, one or more count events aseither a MAX (maximum) or MIN (minimum). Then the count event valuesgenerated by the instrumentation logic are appropriately processed bythe instrumentation server 1699 to maintain maximum and/or minimum countevent values for the specified count event(s) over the predeterminedtime interval represented by the count data storage file 2001.

Referring now to FIG. 26A, there is depicted an exemplary HDL file 440′containing instrumentation descriptor comments identifying count eventsas MAX and MIN count events in accordance with one embodiment of thepresent invention. As indicated by prime notation (′), HDL file 440′ issubstantially similar to HDL file 440 of FIG. 4B, which as noted above,utilizes the syntax of the VHDL hardware description language todescribe the instrumentation entity FXUCHK.

To identify a count event of instrumentation entity FXUCHK as a MAX orMIN count event, the format of counter declaration comments 455 isaugmented to include a keyword to identify the corresponding count eventas a MAX or MIN count event. Thus, a counter declaration comment 455 cangenerally be given as:

-   -   --!! n: <varname> qualifying_signal [+/−] keyword;        where n is an integer denoting which counter event in the        instrumentation module is to be associated with a variable name        varname, qualifying_signal is the name of a signal within a        target design entity utilized to determine when to sample the        count event pulse, +/− denotes either a rising or falling edge        of the qualifying_signal, and keyword has a value of either MAX        or MIN. In the exemplary embodiment shown in FIG. 26A, event( )        is identified as a MIN count event, and event1 is identified as        a MAX count event. Optionally, as further depicted in FIG. 26B,        a count event such as countname( ) can be identified as a MIN or        MAX count event within an instrumentation descriptor comment        1230′ embedded within an HDL file 1220′ defining a design entity        (e.g., entity X) of a simulation model.

As described above with respect to FIG. 4D, instrumentation load tool464 processes the instrumentation entity HDL file 440′ of FIG. 26A orthe embedded instrumentation descriptor comments in the design entityHDL file 1220′ of FIG. 26B to instrument design entities and to createan instrumentation logic block 420 for the simulation model. In apreferred embodiment, instrumentation load tool 464 is configured toorder the count event counters 421 in which count event values aremaintained during a simulation run according to type. For example,instrumentation load tool 464 may first instantiated MIN count eventcounters, then MAX count event counters, and then “regular” count eventcounters. As described further below, the use of a predetermined counterordering by instrumentation load tool 464 in the instantiation ofcounters 421 facilitates subsequent processing of simulation results.

As described with reference to FIGS. 10A-10D, in order to promoteeffective management of instrumentation events, instrumentation loadtool 464 creates, at model build time, a set of eventlist filescontaining information about the exact number and content of theinstrumentation events in a given model. These files, one per class ofevents (fail, count, harvest, etc.), list the particular events in thegiven simulation model. Each simulation model has a unique set ofcorresponding eventlist files that are created at model build time. Asdescribed above with respect to FIG. 15, eventlist files are used by aninstrumentation server (e.g., instrumentation server 1699 of FIG. 16A)to aid in the control, monitoring and analysis of instrumentation eventsin simulation models.

With reference now to FIG. 26C, there is illustrated a count eventeventlist file 1660′ that supports the reporting of MAX and MIN countevents in accordance with the present invention. As described above withrespect to FIG. 15, eventlist file 1660′ contains multiple count eventclass entries 1663, each including a unique, sequential index value1661, and an extended event identifier 1662. Each of indices 1661corresponds to the index of a particular event (in this case count eventCOUNT1) assigned at model build time. Extended event identifiers 1662provide an event name associated with each individual event index.Eventlist file 1660′ thus provides a mapping between the instrumentationevent names and the instrumentation event indices as well as providingan ordering convention for aggregate data for a class of instrumentationevents.

As further shown in FIG. 26C, eventlist file 1660′ includes a MIN/MAXheader 2600 that employs an arbitrarily defined syntax to identify MINand MAX count events within the count event class entries 1663. In thedepicted example, MIN/MAX header 2600 employs the syntax “keyword:integer”, where keyword has a value of MAX or MIN and integer indicatesthe number of count events of the type specified by the keyword. Thus,in the example shown in eventlist file 1660′, MIN/MAX header 2600indicates, following the count event type ordering established byinstrumentation load tool 464 (e.g., MIN, MAX, “regular”) that the countevents associated with index values 1 and 2 are MIN count events and thecount events associated with index values 3, 4 and 5 are MAX countevents. The count events associated with index values 6-9 are neitherMIN nor MAX count events (i.e., are “regular” count event).

By distinguishing MIN and MAX count events from “regular” count eventsin eventlist file 1660′ in this manner, subsequent processing of countevent values can be properly confined to the count event types for whichthe analysis would be meaningful. For example, the post-processing ofsimulation results by CDAE 2100 described above with respect to FIG.21A, which is intended only for “regular” count event values, willexclude the processing of MAX and MIN count events based upon theidentification of MIN and MAX count events in the MIN/MAX header 2600 ofthe respective eventlist file 1660′ characterizing the count events ofeach simulation model.

Referring now to FIG. 26D, there is depicted an exemplary count eventpacket 2610 by which a simulation client may report count event values(including MAX and MIN count events values) of a simulation run to aninstrumentation server 1699 in accordance with the present invention.Similar to aggregate data packet 2010 of FIG. 20B, count event packet2610 includes a model name field 1751, a CRC digital signature field1752, and a data field 1753. Within data field 1753, a cycle count field2011 contains a value representing the number of cycles executed duringthe simulation run from which count event packet 2610 was generated. Aset of count value fields 2012 a-n contain the count values for each ofthe count events instantiated within simulation model 1700 in the orderset forth by the count eventlist file 1660′ created at model build time.

It is possible for an instrumentation server 1699 to determine whichcount events within count event packet 2610 are MIN and MAX count eventsand to thus correctly process count event packet 2610 by reference tocount eventlist file 1660′ or another similar listing of count events.However, in order to expedite processing of count event packets 2610(which may be numerous) by instrumentation server 1699, each count eventpacket 2610 preferably includes an indication of which count valuesrepresent MAX and MIN count event values. In the depicted embodiment,this indication is provided by a combination of a predetermined orderingof count value fields by type and the inclusion of MIN COUNT field 2612and MAX COUNT field 2614 within count event packet 2610. That is, thenumbers of MIN and MAX count value fields 2012 are indicated by MINCOUNT and MAX COUNT fields 2612 and 2614, respectively, and count valuefields 2012 a-n are preferably ordered in the same arbitrary,predetermined order established by instrumentation load tool 464 in theordering of counters 421, for example, first MIN count values, then MAXcount values and finally “regular” count values.

With reference now to FIG. 26E, there is illustrated a high levellogical flowchart of an exemplary process by which instrumentationserver 1699 processes a count event packet 2610 containing MAX and MINcount values in accordance with the present invention. The illustratedprocess is performed, for example, at block 1780 of FIG. 17C followingvalidation of the count event packet 2610 by instrumentation server 1699in accordance with the process of FIG. 17C.

As shown, the process of FIG. 26E begins at block 2650 and then proceedsto block 2652, which shows instrumentation server 1699 accessing thenext (or first) count value field 2012 in the validated count eventpacket 2610. Instrumentation server 1699 then determines at block 2654whether or not the count value field 2012 contains a MIN count value. Asnoted above with reference to FIG. 26D, this determination can be madeby reference to MIN COUNT and MAX COUNT fields 2612 and 2614 and thepredetermined ordering of count value fields 2012. If instrumentationserver 1699 determines that the present count value field 2012 does notcontain a MIN count value, the process proceeds from block 2654 to block2660, which is described below. If, however, a determination is made atblock 2654 that the count value field 2012 under consideration containsa MIN count value, instrumentation server 1699 determines at block 2656whether or not the MIN count value is less than the corresponding valuecurrently stored in the count data storage file 2001 for the currenttime period. If so, instrumentation server 1699 replaces the valuewithin the count data storage file 2001 with the MIN count value in thecurrent count value field 2012, as shown at block 2658. If not, noreplacement is performed.

Referring now to block 2660, instrumentation server 1699 determineswhether or not the current count value field 2012 contains a MAX countvalue. Again, this determination can be made by reference to MIN COUNTand MAX COUNT fields 2612 and 2614 in the count event file 2610 and thepredetermined ordering of count value fields 2012. If instrumentationserver 1699 determines at block 2660 that the present count value field2012 does not contain a MAX count value, the process proceeds from block2660 to block 2670, which is described below. If, however, adetermination is made that the current count value field 2012 underconsideration contains a MAX count value, instrumentation server 1699determines at block 2662 whether or not the MAX count value is greaterthan the corresponding value currently stored in the count data storagefiles 2001 for the current time period. If so, instrumentation server1699 replaces the value within the count data storage file 2001 with theMAX count value in the current count value field 2012, as shown at block2658. If not, no replacement is performed.

Referring now to block 2670, if the current count value field 2012within count event file 2610 is a “regular” count value rather than aMAX or MIN count value, instrumentation server 1699 simply adds thecount value in the current count value field 2012 to the current countevent value in the count data storage file 2001 to update thecorresponding count data storage file 2001. Following block 2670 orfollowing a negative determination at either of blocks 2656 or 2662,instrumentation server 1699 determines at block 2672 whether or not thecount event file 2610 contains an additional count value field 2012 tobe processed or if the last count value field 2012 has been reached. Ifall count value fields 2012 in the count event file 2610 have beenprocessed, the process terminates at block 2674. If, however, fewer thanall count value fields 2012 have been processed, the process shown inFIG. 26E returns to block 2652, which has been described.

As described in detail above with respect to FIGS. 24A-24D, RTX countinstrumentation events may also be created in a high level language(e.g., C or C++) in order to count occurrences of count events that arenot easily detected by HDL-based instrumentation logic. By applying theprinciples of the present invention described above with respect to HDLcount instrumentation events, a second embodiment of the presentinvention supports the definition and recording of MIN and MAX RTX countinstrumentation events. As noted above, such RTX count instrumentationevents are particularly useful in collecting statistics related to theRTX input parameter values for each executed testcase in a simulationrun.

MIN and MAX RTX count instrumentation events may advantageously becreated utilizing the same syntax described above with respect to FIG.24A. Specifically, the set of legal values for type parameter 2409 isdefined to include, in addition to count, fail and harvest, twoadditional types, namely, MIN count and MAX count. As implied by thesetype identifiers, specifying MIN count for type parameter 2409 willcause the creation of a MIN RTX count instrumentation event, andspecifying MAX count for type parameter 2409 will cause the creation ofa MAX RTX count instrumentation event.

As described above, in response to the definition of RTX instrumentationevents including MIN and MAX RTX count instrumentation events utilizingthe syntax of FIG. 24A, API entry point create_event 2400 (FIG. 24B)creates an RTX instrumentation eventlist data structure 2442, whichlists the extended event identifiers and event types for each of theobject RTX instrumentation events. API entry point create_event 2400preferably includes within RTX instrumentation eventlist data structure2442 an indication 2443 a of a number of MIN RTX count instrumentationevents and an indication 2443b of a number of MAX RTX countinstrumentation events. When API entry point write_eventlists( ) 2407creates the RTX instrumentation eventlist files from RTX instrumentationeventlist data structure 2442 as shown at block 2464 of FIG. 24C,write_eventlists( ) 2407 includes in the RTX instrumentation eventlistfiles the number of MIN RTX count instrumentation events and MAX RTXcount instrumentation events, as described above with respect to FIG.26C.

As noted above, when commissioning eventlist files, instrumentationserver 1699 generally does not distinguish between eventlist files forHDL instrumentation events generated during model build and eventlistfiles for RTX instrumentation events generated by RTX 1702.Instrumentation server 1699 can therefore support and process RTXinstrumentation events, including MIN and MAX RTX count instrumentationevents, utilizing the same process described above with respect to FIGS.17C and 26C.

As has been described, the present invention provides a method, systemand program product for defining maximum and minimum count events in HDLinstrumentation logic and rtx instrumentation code of a simulation modeland for recording maximum and minimum count event values of simulationruns. By recording maximum and minimum count values in the manner hereindescribed for subsequent examination and analysis, simulation engineersand designers are able to better understand the characteristics andoperation of the simulation model of a design.

Although the present invention has been described specifically withrespect to MAX and MIN count events, such count events, for which valuesare not accumulated with previously obtained count event values, maymore generally be referred to as “outlying count events,” which termshould be understood to encompass both MAX and MIN count events.

While the invention has been particularly shown as described withreference to a preferred embodiment, it will be understood by thoseskilled in the art that various changes in form and detail may be madetherein without departing from the spirit and scope of the invention.For example, one of the embodiments of the invention can be implementedas sets of instructions resident in random access memory 28 of one ormore computer systems configured generally as described in FIG. 1 andFIG. 2. Until required by computer system 10, the set of instructionsmay be stored in another computer readable storage device, such as diskdrive 33 or in a removable storage device such as an optical disk foreventual use in a CD-ROM drive or a floppy disk for eventual use in afloppy disk drive. The set of instructions may be referred to as acomputer program product. Further, the set of instructions can be storedin the memory of another computer and transmitted over a local areanetwork or a wide area network, such as the Internet, when desired bythe user.

1. A method of simulation processing in a data processing system, saidmethod comprising: receiving, within instrumentation code, one or morestatements describing a plurality of count events each corresponding toa respective sequence of signal values within a design entity of an HDLsimulation model representing a design to be simulated, the plurality ofcount events including a first count event and a second count event,wherein the instrumentation code identifies said first count event as anoutlying count event that is one of a set consisting of a minimum countevent and a maximum count event and the second count event is notidentified as an outlying count event in the instrumentation code;simulating the design utilizing the HDL simulation model, wherein thesimulating includes separately counting occurrences of each of saidplurality of count events to obtain a respective count event value foreach of said plurality of count events; reporting simulation result dataobtained from said simulating, wherein said simulation result dataincludes said count event values of said plurality of count events, andwherein said simulation result data indicates that the count event valueof the outlying count event is associated with one of the set consistingof a minimum count event and a maximum count event; and in response toreceipt of said simulation result data, recording said count eventvalues within a data storage subsystem, wherein said recording includes:in response to the simulation result data indicating the count eventvalue of the outlying count event is associated with one of the setconsisting of a minimum count event and a maximum count event,determining whether said count event value of said outlying count eventexceeds a previously reported count event value for said outlying countevent; responsive to a determination of whether said count event valueof said outlying count event exceeds the previously reported count eventvalue of the outlying count event, replacing said previously reportedcount event value of the outlying count event with said count eventvalue of the outlying count event reported in the simulation resultdata.
 2. The method of claim 1, wherein said recording comprisesrecording said count event value of the outlying count event only ifsaid count event exceeds said previously reported count event value. 3.The method of claim 1, wherein said recording comprises recording saidcount event value of the outlying count event only if said count eventvalue does not exceed said previously reported count event value.
 4. Themethod of claim 1, wherein: said outlying count event comprises a firstoutlying count event; said reporting simulation result data comprises:reporting a plurality of count event values of a plurality of outlyingcount events including said first outlying count event and receiving atleast another count event value of another count event; and reporting anindication of a number of said count event values of said plurality ofoutlying count events.
 5. The method of claim 1, said recording furthercomprising accumulating a count event value of said second count eventwith a previously recorded count event value of said second count event.